From 5fd8ddaf43e82ef9b14ecf06a0649d7a458dd77b Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 23 Mar 2026 18:59:45 +0800
Subject: [PATCH 01/10] draft

---
 embodichain/lab/sim/objects/rigid_object.py   |  67 +++
 .../graspkit/pg_grasp/antipodal_annotator.py  | 489 ++++++++++++++++++
 .../graspkit/pg_grasp/antipodal_sampler.py    | 231 +++++++++
 examples/sim/demo/grasp_mug.py                | 257 +++++++++
 4 files changed, 1044 insertions(+)
 create mode 100644 embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
 create mode 100644 embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
 create mode 100644 examples/sim/demo/grasp_mug.py

diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index 565c5bf4..62207baa 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -34,6 +34,11 @@
 from embodichain.utils.math import convert_quat
 from embodichain.utils.math import matrix_from_quat, quat_from_matrix, matrix_from_euler
 from embodichain.utils import logger
+from embodichain.toolkits.graspkit.pg_grasp.antipodal_annotator import (
+    GraspAnnotator,
+    GraspAnnotatorCfg,
+)
+import torch.nn.functional as F
 
 
 @dataclass
@@ -1108,3 +1113,65 @@ def destroy(self) -> None:
             arenas = [env]
         for i, entity in enumerate(self._entities):
             arenas[i].remove_actor(entity)
+
+    def get_grasp_pose(
+        self,
+        cfg: GraspAnnotatorCfg,
+        approach_direction: torch.Tensor = None,
+        is_visual: bool = False,
+    ) -> torch.Tensor:
+        if approach_direction is None:
+            approach_direction = torch.tensor(
+                [0, 0, -1], dtype=torch.float32, device=self.device
+            )
+        approach_direction = F.normalize(approach_direction, dim=-1)
+        if hasattr(self, "_grasp_annotator") is False:
+            self._grasp_annotator = GraspAnnotator(cfg=cfg)
+        if hasattr(self, "_hit_point_pairs") is False or cfg.force_regenerate:
+            vertices = torch.tensor(
+                self._entities[0].get_vertices(),
+                dtype=torch.float32,
+                device=self.device,
+            )
+            triangles = torch.tensor(
+                self._entities[0].get_triangles(), dtype=torch.int32, device=self.device
+            )
+            scale = torch.tensor(
+                self._entities[0].get_body_scale(),
+                dtype=torch.float32,
+                device=self.device,
+            )
+            vertices = vertices * scale
+            self._hit_point_pairs = self._grasp_annotator.annotate(vertices, triangles)
+
+        poses = self.get_local_pose(to_matrix=True)
+        poses = torch.as_tensor(poses, dtype=torch.float32, device=self.device)
+        grasp_poses = []
+        open_lengths = []
+        for pose in poses:
+            grasp_pose, open_length = self._grasp_annotator.get_approach_grasp_poses(
+                self._hit_point_pairs, pose, approach_direction
+            )
+            grasp_poses.append(grasp_pose)
+            open_lengths.append(open_length)
+        grasp_poses = torch.cat(
+            [grasp_pose.unsqueeze(0) for grasp_pose in grasp_poses], dim=0
+        )
+
+        if is_visual:
+            vertices = self._entities[0].get_vertices()
+            triangles = self._entities[0].get_triangles()
+            scale = self._entities[0].get_body_scale()
+            vertices = vertices * scale
+            GraspAnnotator.visualize_grasp_pose(
+                vertices=torch.tensor(
+                    vertices, dtype=torch.float32, device=self.device
+                ),
+                triangles=torch.tensor(
+                    triangles, dtype=torch.int32, device=self.device
+                ),
+                obj_pose=poses[0],
+                grasp_pose=grasp_poses[0],
+                open_length=open_lengths[0],
+            )
+        return grasp_poses
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
new file mode 100644
index 00000000..4852879e
--- /dev/null
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
@@ -0,0 +1,489 @@
+import os
+import argparse
+import open3d as o3d
+import time
+from pathlib import Path
+from typing import Any, cast
+import torch
+import numpy as np
+import trimesh
+
+import viser
+import viser.transforms as tf
+from embodichain.utils import logger
+from dataclasses import dataclass
+from embodichain.toolkits.graspkit.pg_grasp.antipodal_sampler import (
+    AntipodalSampler,
+    AntipodalSamplerCfg,
+)
+import hashlib
+import torch.nn.functional as F
+import tempfile
+
+
+@dataclass
+class GraspAnnotatorCfg:
+    viser_port: int = 15531
+    use_largest_connected_component: bool = False
+    antipodal_sampler_cfg: AntipodalSamplerCfg = AntipodalSamplerCfg()
+    force_regenerate: bool = False
+    max_deviation_angle: float = np.pi / 12
+
+
+@dataclass
+class SelectResult:
+    vertex_indices: np.ndarray | None = None
+    face_indices: np.ndarray | None = None
+    vertices: np.ndarray | None = None
+    faces: np.ndarray | None = None
+
+
+class GraspAnnotator:
+    def __init__(self, cfg: GraspAnnotatorCfg = GraspAnnotatorCfg()) -> None:
+        self.cfg = cfg
+        self.antipodal_sampler = AntipodalSampler(cfg=cfg.antipodal_sampler_cfg)
+
+    def annotate(self, vertices: torch.Tensor, triangles: torch.Tensor):
+        cache_path = self._get_cache_dir(vertices, triangles)
+        if os.path.exists(cache_path) and not self.cfg.force_regenerate:
+            logger.log_info(
+                f"Found existing antipodal retult. Loading cached antipodal pairs from {cache_path}"
+            )
+            hit_point_pairs = torch.tensor(
+                np.load(cache_path), dtype=torch.float32, device=vertices.device
+            )
+            return hit_point_pairs
+        else:
+            logger.log_info(
+                f"[Viser] *****Annotate grasp region in http://localhost:{self.cfg.viser_port}"
+            )
+
+        self.mesh = trimesh.Trimesh(
+            vertices=vertices.to("cpu").numpy(),
+            faces=triangles.to("cpu").numpy(),
+            process=False,
+            force="mesh",
+        )
+        self.device = vertices.device
+
+        server = viser.ViserServer(port=self.cfg.viser_port)
+        server.gui.configure_theme(brand_color=(130, 0, 150))
+        server.scene.set_up_direction("+z")
+
+        mesh_handle = server.scene.add_mesh_trimesh(name="/mesh", mesh=self.mesh)
+        selected_overlay: viser.GlbHandle | None = None
+        selection: SelectResult = SelectResult()
+
+        hit_point_pairs = None
+        return_flag = False
+
+        @server.on_client_connect
+        def _(client: viser.ClientHandle) -> None:
+            nonlocal mesh_handle
+            nonlocal selected_overlay
+            nonlocal selection
+
+            # client.camera.position = np.array([0.0, 0.0, -0.5])
+            # client.camera.wxyz = np.array([1.0, 0.0, 0.0, 0.0])
+
+            select_button = client.gui.add_button(
+                "Rect Select Region", icon=viser.Icon.PAINT
+            )
+            confirm_button = client.gui.add_button("Confirm Selection")
+
+            @select_button.on_click
+            def _(_evt: viser.GuiEvent) -> None:
+                select_button.disabled = True
+
+                @client.scene.on_pointer_event(event_type="rect-select")
+                def _(event: viser.ScenePointerEvent) -> None:
+                    nonlocal mesh_handle
+                    nonlocal selected_overlay
+                    nonlocal selection
+                    nonlocal hit_point_pairs
+                    client.scene.remove_pointer_callback()
+
+                    proj, depth = GraspAnnotator._project_vertices_to_screen(
+                        cast(np.ndarray, self.mesh.vertices),
+                        mesh_handle,
+                        event.client.camera,
+                    )
+
+                    lower = np.minimum(
+                        np.array(event.screen_pos[0]), np.array(event.screen_pos[1])
+                    )
+                    upper = np.maximum(
+                        np.array(event.screen_pos[0]), np.array(event.screen_pos[1])
+                    )
+                    vertex_mask = ((proj >= lower) & (proj <= upper)).all(axis=1) & (
+                        depth > 1e-6
+                    )
+
+                    selection = GraspAnnotator._extract_selection(
+                        self.mesh, vertex_mask, self.cfg.use_largest_connected_component
+                    )
+                    if selection.vertices is None:
+                        logger.log_warning("[Selection] No vertices selected.")
+                        return
+
+                    color_mesh = self.mesh.copy()
+                    used_vertex_indices = selection.vertex_indices
+                    vertex_colors = np.tile(
+                        np.array([[0.85, 0.85, 0.85, 1.0]]),
+                        (self.mesh.vertices.shape[0], 1),
+                    )
+                    vertex_colors[used_vertex_indices] = np.array(
+                        [0.56, 0.17, 0.92, 1.0]
+                    )
+                    color_mesh.visual.vertex_colors = vertex_colors  # type: ignore
+                    mesh_handle = server.scene.add_mesh_trimesh(
+                        name="/mesh", mesh=color_mesh
+                    )
+
+                    if selected_overlay is not None:
+                        selected_overlay.remove()
+                    selected_mesh = trimesh.Trimesh(
+                        vertices=selection.vertices,
+                        faces=selection.faces,
+                        process=False,
+                    )
+                    selected_mesh.visual.face_colors = (0.9, 0.2, 0.2, 0.65)  # type: ignore
+                    selected_overlay = server.scene.add_mesh_trimesh(
+                        name="/selected", mesh=selected_mesh
+                    )
+                    logger.log_info(
+                        f"[Selection] Selected {selection.vertex_indices.size} vertices and {selection.face_indices.size} faces."
+                    )
+
+                    hit_point_pairs = self.antipodal_sampler.sample(
+                        torch.tensor(selection.vertices, device=self.device),
+                        torch.tensor(selection.faces, device=self.device),
+                    )
+                    extended_hit_point_pairs = GraspAnnotator._extend_hit_point_pairs(
+                        hit_point_pairs
+                    )
+                    server.scene.add_line_segments(
+                        name="/antipodal_pairs",
+                        points=extended_hit_point_pairs.to("cpu").numpy(),
+                        colors=(20, 200, 200),
+                        line_width=1.5,
+                    )
+
+                @client.scene.on_pointer_callback_removed
+                def _() -> None:
+                    select_button.disabled = False
+
+            @confirm_button.on_click
+            def _(_evt: viser.GuiEvent) -> None:
+                nonlocal return_flag
+                if selection.vertices is None:
+                    logger.log_warning("[Selection] No vertex selected.")
+                    return
+                else:
+                    logger.log_info(
+                        f"[Selection] {selection.vertices.shape[0]}vertices selected. Generating antipodal point pairs."
+                    )
+                    return_flag = True
+
+        while True:
+            if return_flag:
+                # save result to cache
+                if hit_point_pairs is not None:
+                    self._save_cache(cache_path, hit_point_pairs)
+                break
+            time.sleep(0.5)
+        return hit_point_pairs
+
+    def _get_cache_dir(self, vertices: torch.Tensor, triangles: torch.Tensor):
+        vert_bytes = vertices.to("cpu").numpy().tobytes()
+        face_bytes = triangles.to("cpu").numpy().tobytes()
+        md5_hash = hashlib.md5(vert_bytes + face_bytes).hexdigest()
+        cache_path = os.path.join(
+            tempfile.gettempdir(), f"antipodal_cache_{md5_hash}.npy"
+        )
+        return cache_path
+
+    def _save_cache(self, cache_path: str, hit_point_pairs: torch.Tensor):
+        np.save(cache_path, hit_point_pairs.cpu().numpy().astype(np.float32))
+
+    @staticmethod
+    def _extend_hit_point_pairs(hit_point_pairs: torch.Tensor):
+        origin_points = hit_point_pairs[:, 0, :]
+        hit_points = hit_point_pairs[:, 1, :]
+        mid_points = (origin_points + hit_points) / 2
+        point_diff = hit_points - origin_points
+        extended_origin = mid_points - 0.8 * point_diff
+        extended_hit = mid_points + 0.8 * point_diff
+        extended_point_pairs = torch.cat(
+            [extended_origin[:, None, :], extended_hit[:, None, :]], dim=1
+        )
+        return extended_point_pairs
+
+    @staticmethod
+    def _project_vertices_to_screen(
+        vertices_mesh: np.ndarray,
+        mesh_handle: viser.GlbHandle,
+        camera: Any,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        T_world_mesh = tf.SE3.from_rotation_and_translation(
+            tf.SO3(np.asarray(mesh_handle.wxyz)),
+            np.asarray(mesh_handle.position),
+        )
+        vertices_world_h = (
+            T_world_mesh.as_matrix()
+            @ np.hstack([vertices_mesh, np.ones((vertices_mesh.shape[0], 1))]).T
+        ).T
+        vertices_world = vertices_world_h[:, :3]
+
+        T_camera_world = tf.SE3.from_rotation_and_translation(
+            tf.SO3(np.asarray(camera.wxyz)),
+            np.asarray(camera.position),
+        ).inverse()
+        vertices_camera_h = (
+            T_camera_world.as_matrix()
+            @ np.hstack([vertices_world, np.ones((vertices_world.shape[0], 1))]).T
+        ).T
+        vertices_camera = vertices_camera_h[:, :3]
+
+        fov = float(camera.fov)
+        aspect = float(camera.aspect)
+        projected = vertices_camera[:, :2] / np.maximum(vertices_camera[:, 2:3], 1e-8)
+        projected /= np.tan(fov / 2.0)
+        projected[:, 0] /= aspect
+        projected = (1.0 + projected) / 2.0
+        return projected, vertices_camera[:, 2]
+
+    def _extract_selection(
+        mesh: trimesh.Trimesh,
+        vertex_mask: np.ndarray,
+        largest_component: bool,
+    ) -> SelectResult:
+        def _largest_connected_face_component(face_ids: np.ndarray) -> np.ndarray:
+            if face_ids.size <= 1:
+                return face_ids
+
+            face_id_set = set(face_ids.tolist())
+            parent: dict[int, int] = {
+                int(face_id): int(face_id) for face_id in face_ids
+            }
+
+            def find(x: int) -> int:
+                root = x
+                while parent[root] != root:
+                    root = parent[root]
+                while parent[x] != x:
+                    x_parent = parent[x]
+                    parent[x] = root
+                    x = x_parent
+                return root
+
+            def union(a: int, b: int) -> None:
+                ra, rb = find(a), find(b)
+                if ra != rb:
+                    parent[rb] = ra
+
+            face_adjacency = cast(np.ndarray, mesh.face_adjacency)
+            for face_a, face_b in face_adjacency:
+                if int(face_a) in face_id_set and int(face_b) in face_id_set:
+                    union(int(face_a), int(face_b))
+
+            groups: dict[int, list[int]] = {}
+            for face_id in face_ids:
+                root = find(int(face_id))
+                groups.setdefault(root, []).append(int(face_id))
+
+            largest_group = max(groups.values(), key=len)
+            return np.array(largest_group, dtype=np.int32)
+
+        faces = cast(np.ndarray, mesh.faces)
+        face_mask = np.all(vertex_mask[faces], axis=1)
+
+        face_indices = np.flatnonzero(face_mask)
+        if face_indices.size == 0:
+            return SelectResult()
+        if largest_component:
+            face_indices = _largest_connected_face_component(face_indices)
+            if face_indices.size == 0:
+                return SelectResult()
+
+        selected_face_vertices = faces[face_indices]
+        vertex_indices = np.unique(selected_face_vertices.reshape(-1))
+
+        old_to_new = np.full(mesh.vertices.shape[0], -1, dtype=np.int32)
+        old_to_new[vertex_indices] = np.arange(vertex_indices.size, dtype=np.int32)
+
+        sub_vertices = np.asarray(mesh.vertices)[vertex_indices]
+        sub_faces = np.asarray(old_to_new)[selected_face_vertices]
+
+        return SelectResult(
+            vertex_indices=vertex_indices,
+            face_indices=face_indices,
+            vertices=sub_vertices,
+            faces=sub_faces,
+        )
+
+    @staticmethod
+    def _apply_transform(points: torch.Tensor, transform: torch.Tensor) -> torch.Tensor:
+        r = transform[:3, :3]
+        t = transform[:3, 3]
+        return points @ r.T + t
+
+    def get_approach_grasp_poses(
+        self,
+        hit_point_pairs: torch.Tensor,
+        object_pose: torch.Tensor,
+        approach_direction: torch.Tensor,
+    ) -> torch.Tensor:
+        """Get grasp pose given approach direction
+
+        Args:
+            hit_point_pairs (torch.Tensor): (N, 2, 3) tensor of N antipodal point pairs. Each pair consists of a hit point and its corresponding surface point.
+            object_pose (torch.Tensor): (4, 4) homogeneous transformation matrix representing the pose of the object in the world frame.
+            approach_direction (torch.Tensor): (3,) unit vector representing the desired approach direction of the gripper in the world frame.
+
+        Returns:
+            torch.Tensor: (4, 4) homogeneous transformation matrix representing the grasp pose in the world frame that aligns the gripper's approach direction with the given approach_direction. Returns None if no valid grasp pose can be found.
+        """
+        origin_points = hit_point_pairs[:, 0, :]
+        hit_points = hit_point_pairs[:, 1, :]
+        print("origin_points dtype:", origin_points.dtype)
+        print("object_pose dtype:", object_pose.dtype)
+        origin_points_ = self._apply_transform(origin_points, object_pose)
+        hit_points_ = self._apply_transform(hit_points, object_pose)
+        centers = (origin_points_ + hit_points_) / 2
+        center = centers.mean(dim=0)
+
+        # get best grasp pose
+        grasp_x = F.normalize(hit_points_ - origin_points_, dim=-1)
+        cos_angle = torch.clamp((grasp_x * approach_direction).sum(dim=-1), -1.0, 1.0)
+        positive_angle = torch.abs(torch.acos(cos_angle))
+        antipodal_length = torch.norm(hit_points_ - origin_points_, dim=-1)
+        length_cost = 1 - antipodal_length / antipodal_length.max()
+        angle_cost = torch.abs(positive_angle - 0.5 * torch.pi) / (0.5 * torch.pi)
+        center_distance = torch.norm(centers - center, dim=-1)
+        center_cost = center_distance / center_distance.max()
+        total_cost = 0.4 * angle_cost + 0.3 * length_cost + 0.3 * center_cost
+        best_idx = torch.argmin(total_cost)
+
+        best_open_length = torch.norm(hit_points_[best_idx] - origin_points_[best_idx])
+        best_grasp_x = grasp_x[best_idx]
+        best_grasp_center = centers[best_idx]
+        best_grasp_y = torch.cross(approach_direction, best_grasp_x, dim=0)
+        best_grasp_y = F.normalize(best_grasp_y, dim=-1)
+        best_grasp_z = torch.cross(best_grasp_x, best_grasp_y, dim=0)
+        best_grasp_z = F.normalize(best_grasp_z, dim=-1)
+        grasp_pose = torch.eye(4, device=hit_point_pairs.device, dtype=torch.float32)
+        grasp_pose[:3, 0] = best_grasp_x
+        grasp_pose[:3, 1] = best_grasp_y
+        grasp_pose[:3, 2] = best_grasp_z
+        grasp_pose[:3, 3] = best_grasp_center
+        return grasp_pose, best_open_length
+
+    @staticmethod
+    def visualize_grasp_pose(
+        vertices: torch.Tensor,
+        triangles: torch.Tensor,
+        obj_pose: torch.Tensor,
+        grasp_pose: torch.Tensor,
+        open_length: float,
+    ):
+        mesh = o3d.geometry.TriangleMesh(
+            vertices=o3d.utility.Vector3dVector(vertices.to("cpu").numpy()),
+            triangles=o3d.utility.Vector3iVector(triangles.to("cpu").numpy()),
+        )
+        mesh.compute_vertex_normals()
+        mesh.paint_uniform_color([0.3, 0.6, 0.3])
+        mesh.transform(obj_pose.to("cpu").numpy())
+        vertices_ = torch.tensor(
+            np.asarray(mesh.vertices), device=vertices.device, dtype=vertices.dtype
+        )
+        mesh_scale = (vertices_.max(dim=0)[0] - vertices_.min(dim=0)[0]).max().item()
+        groud_plane = o3d.geometry.TriangleMesh.create_cylinder(
+            radius=mesh_scale, height=0.01 * mesh_scale
+        )
+        groud_plane.compute_vertex_normals()
+        center = vertices_.mean(dim=0)
+        z_sim = vertices_.min(dim=0)[0][2].item()
+        groud_plane.translate(
+            (center[0].item(), center[1].item(), z_sim - 0.005 * mesh_scale)
+        )
+
+        draw_thickness = 0.02 * mesh_scale
+        draw_length = 0.3 * mesh_scale
+        grasp_finger1 = o3d.geometry.TriangleMesh.create_box(
+            draw_thickness, draw_thickness, draw_length
+        )
+        grasp_finger1.translate(
+            (-0.5 * draw_thickness, -0.5 * draw_thickness, -0.5 * draw_length)
+        )
+        grasp_finger2 = o3d.geometry.TriangleMesh.create_box(
+            draw_thickness, draw_thickness, draw_length
+        )
+        grasp_finger2.translate(
+            (-0.5 * draw_thickness, -0.5 * draw_thickness, -0.5 * draw_length)
+        )
+        grasp_finger1.translate((-open_length / 2, 0, -0.25 * draw_length))
+        grasp_finger2.translate((open_length / 2, 0, -0.25 * draw_length))
+        grasp_root1 = o3d.geometry.TriangleMesh.create_box(
+            open_length, draw_thickness, draw_thickness
+        )
+        grasp_root1.translate(
+            (-open_length / 2, -0.5 * draw_thickness, -0.5 * draw_thickness)
+        )
+        grasp_root1.translate((0, 0, -0.75 * draw_length))
+        grasp_root2 = o3d.geometry.TriangleMesh.create_box(
+            draw_thickness, draw_thickness, draw_length
+        )
+        grasp_root2.translate(
+            (-0.5 * draw_thickness, -0.5 * draw_thickness, -0.5 * draw_length)
+        )
+        grasp_root2.translate((0, 0, -1.25 * draw_length))
+
+        grasp_visual = grasp_finger1 + grasp_finger2 + grasp_root1 + grasp_root2
+        grasp_visual.paint_uniform_color([0.8, 0.2, 0.8])
+        grasp_visual.transform(grasp_pose.to("cpu").numpy())
+        o3d.visualization.draw_geometries(
+            [grasp_visual, mesh, groud_plane],
+            window_name="Grasp Pose Visualization",
+            mesh_show_back_face=True,
+        )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Viser mesh 标注工具：框选并导出对应顶点与三角面"
+    )
+    parser.add_argument(
+        "--mesh", type=Path, required=True, help="输入 mesh 文件路径，例如 mug.obj"
+    )
+    parser.add_argument("--scale", type=float, default=1.0, help="加载后整体缩放系数")
+    parser.add_argument("--port", type=int, default=12151, help="viser 服务端口")
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("outputs/mesh_annotations"),
+        help="标注结果导出目录",
+    )
+    parser.add_argument(
+        "--largest-component",
+        action="store_true",
+        help="只保留框选结果中的最大连通块（常用于稳定提取把手等局部）",
+    )
+    args = parser.parse_args()
+
+    mesh = trimesh.load(args.mesh, process=False, force="mesh")
+    vertices = mesh.vertices * args.scale
+    triangles = mesh.faces
+    cfg = GraspAnnotatorCfg(
+        force_regenerate=True,
+    )
+    tool = GraspAnnotator(cfg=cfg)
+    hit_point_pairs = tool.annotate(
+        vertices=torch.from_numpy(vertices).float(),
+        triangles=torch.from_numpy(triangles).long(),
+    )
+    logger.log_info(f"Sample {hit_point_pairs.shape[0]} antipodal point pairs.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
new file mode 100644
index 00000000..1eb3ec61
--- /dev/null
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
@@ -0,0 +1,231 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+import open3d as o3d
+import open3d.core as o3c
+from dataclasses import dataclass
+from embodichain.utils import logger
+
+
+@dataclass
+class AntipodalSamplerCfg:
+    n_sample: int = 10000
+    """surface point sample number"""
+    max_angle: float = np.pi / 12
+    """maximum angle (in radians) to randomly disturb the ray direction for antipodal point sampling, used to increase the diversity of sampled antipodal points. Note that setting max_angle to 0 will disable the random disturbance and sample antipodal points strictly along the surface normals, which may result in less diverse antipodal points and may not be ideal for all objects or grasping scenarios."""
+    max_length: float = 0.1
+    """maximum gripper open width, used to filter out antipodal points that are too far apart to be grasped"""
+    min_length: float = 0.001
+    """minimum gripper open width, used to filter out antipodal points that are too close to be grasped"""
+
+
+class AntipodalSampler:
+    def __init__(
+        self,
+        cfg: AntipodalSamplerCfg = AntipodalSamplerCfg(),
+    ):
+        self.mesh: o3d.t.geometry.TriangleMesh | None = None
+        self.cfg = cfg
+
+    def sample(self, vertices: torch.Tensor, faces: torch.Tensor) -> torch.Tensor:
+        """Get sample Antipodal point pair
+
+        Returns:
+            hit_point_pairs: [N, 2, 3] tensor of N antipodal point pairs. Each pair consists of a hit point and its corresponding surface point.
+        """
+        # update mesh
+        self.mesh = o3d.t.geometry.TriangleMesh()
+        self.mesh.vertex.positions = o3c.Tensor(
+            vertices.to("cpu").numpy(), dtype=o3c.float32
+        )
+        self.mesh.triangle.indices = o3c.Tensor(
+            faces.to("cpu").numpy(), dtype=o3c.int32
+        )
+        self.mesh.compute_vertex_normals()
+        # sample points and normals
+        sample_pcd = self.mesh.sample_points_uniformly(
+            number_of_points=self.cfg.n_sample
+        )
+        sample_points = torch.tensor(
+            sample_pcd.point.positions.numpy(),
+            device=vertices.device,
+            dtype=vertices.dtype,
+        )
+        sample_normals = torch.tensor(
+            sample_pcd.point.normals.numpy(),
+            device=vertices.device,
+            dtype=vertices.dtype,
+        )
+        # generate rays
+        ray_direc = -sample_normals
+        ray_origin = (
+            sample_points + 1e-3 * ray_direc
+        )  # Offset ray origin slightly along the normal to avoid self-intersection
+        disturb_direc = AntipodalSampler._random_rotate_unit_vectors(
+            ray_direc, max_angle=self.cfg.max_angle
+        )
+        ray_origin = torch.vstack([ray_origin, ray_origin])
+        ray_direc = torch.vstack([ray_direc, disturb_direc])
+        # casting
+        return self.get_raycast_result(
+            ray_origin,
+            ray_direc,
+            surface_origin=torch.vstack([sample_points, sample_points]),
+        )
+
+    def get_raycast_result(
+        self,
+        ray_origin: torch.Tensor,
+        ray_direc: torch.Tensor,
+        surface_origin: torch.Tensor,
+    ):
+        if ray_origin.ndim != 2 or ray_origin.shape[-1] != 3:
+            raise ValueError("ray_origin must have shape [N, 3]")
+        if ray_direc.ndim != 2 or ray_direc.shape[-1] != 3:
+            raise ValueError("ray_direc must have shape [N, 3]")
+        if ray_origin.shape[0] != ray_direc.shape[0]:
+            raise ValueError(
+                "ray_origin and ray_direc must have the same number of rays"
+            )
+        if ray_origin.shape[0] != surface_origin.shape[0]:
+            raise ValueError(
+                "ray_origin and surface_origin must have the same number of rays"
+            )
+
+        scene = o3d.t.geometry.RaycastingScene()
+        scene.add_triangles(self.mesh)
+
+        rays = torch.cat([ray_origin, ray_direc], dim=-1)
+        rays_o3d = o3c.Tensor(rays.detach().to("cpu").numpy(), dtype=o3c.float32)
+
+        ans = scene.cast_rays(rays_o3d)
+        t_hit = torch.from_numpy(ans["t_hit"].numpy()).to(
+            device=ray_origin.device, dtype=ray_origin.dtype
+        )
+        hit_mask = torch.logical_and(
+            t_hit > self.cfg.min_length, t_hit < self.cfg.max_length
+        )
+        hit_points = ray_origin[hit_mask] + t_hit[hit_mask, None] * ray_direc[hit_mask]
+        hit_origins = surface_origin[hit_mask]
+        hit_point_pairs = torch.cat(
+            [hit_points[:, None, :], hit_origins[:, None, :]], dim=1
+        )
+        hit_point_pairs = hit_point_pairs.to(dtype=torch.float32)
+        return hit_point_pairs
+
+    @staticmethod
+    def _random_rotate_unit_vectors(
+        vectors: torch.Tensor,
+        max_angle: float,
+        degrees: bool = False,
+        eps: float = 1e-8,
+    ) -> torch.Tensor:
+        """
+        Apply random small rotations to a batch of unit vectors [N, 3].
+
+        Args:
+            vectors: [N, 3], unit vectors
+            max_angle: Maximum rotation angle
+            degrees: If True, `max_angle` is given in degrees
+            eps: Numerical stability constant
+
+        Returns:
+            rotated: [N, 3], rotated unit vectors
+        """
+        assert vectors.ndim == 2 and vectors.shape[-1] == 3, "vectors must be [N, 3]"
+
+        v = F.normalize(vectors, dim=-1)
+
+        if degrees:
+            max_angle = torch.deg2rad(
+                torch.tensor(max_angle, dtype=v.dtype, device=v.device)
+            ).item()
+
+        n = v.shape[0]
+
+        # 1) Generate a random direction for each vector
+        #   then project it onto the plane perpendicular to v to get the rotation axis k
+        rand_dir = torch.randn_like(v) + eps
+        proj = (rand_dir * v).sum(dim=-1, keepdim=True) * v
+        k = rand_dir - proj
+        k = F.normalize(k, dim=-1)
+
+        # 2) Sample rotation angles in the range [eps, max_angle]
+        theta = (
+            torch.rand(n, 1, device=v.device, dtype=v.dtype) * (max_angle - eps) + eps
+        )
+
+        # 3) Rodrigues' rotation formula
+        # R(v) = v*cosθ + (k×v)*sinθ + k*(k·v)*(1-cosθ)
+        # Since k ⟂ v, the last term is theoretically 0, but keeping the general formula is more robust
+        cos_t = torch.cos(theta)
+        sin_t = torch.sin(theta)
+
+        kv = (k * v).sum(dim=-1, keepdim=True)
+        rotated = v * cos_t + torch.cross(k, v, dim=-1) * sin_t + k * kv * (1.0 - cos_t)
+
+        return F.normalize(rotated, dim=-1)
+
+    def visualize(self, hit_point_pairs: torch.Tensor):
+        if self.mesh is None:
+            logger.log_warning("Mesh is not initialized. Cannot visualize.")
+            return
+
+        if hit_point_pairs.shape[0] == 0:
+            raise ValueError("No point pairs to visualize")
+        origin_points = hit_point_pairs[:, 0, :]
+        hit_points = hit_point_pairs[:, 1, :]
+
+        origin_points_np = origin_points.to("cpu").numpy()
+        hit_points_np = hit_points.detach().to("cpu").numpy()
+
+        n_pairs = hit_point_pairs.shape[0]
+        line_indices = np.stack(
+            [np.arange(n_pairs), np.arange(n_pairs) + n_pairs], axis=1
+        )
+
+        mesh_legacy = self.mesh.to_legacy()
+        mesh_legacy.compute_vertex_normals()
+        mesh_legacy.paint_uniform_color([0.8, 0.8, 0.8])
+
+        origin_pcd = o3d.geometry.PointCloud()
+        origin_pcd.points = o3d.utility.Vector3dVector(origin_points_np)
+        origin_pcd.colors = o3d.utility.Vector3dVector(
+            np.tile(np.array([[0.1, 0.4, 1.0]]), (n_pairs, 1))
+        )
+
+        hit_pcd = o3d.geometry.PointCloud()
+        hit_pcd.points = o3d.utility.Vector3dVector(hit_points_np)
+        hit_pcd.colors = o3d.utility.Vector3dVector(
+            np.tile(np.array([[1.0, 0.2, 0.2]]), (n_pairs, 1))
+        )
+
+        line_set = o3d.geometry.LineSet()
+        mid_points = (origin_points_np + hit_points_np) / 2
+        point_diff = hit_points_np - origin_points_np
+        draw_origin = mid_points - 0.6 * point_diff
+        draw_end = mid_points + 0.6 * point_diff
+        draw_pointpair = np.concatenate([draw_origin, draw_end], axis=0)
+        line_set.points = o3d.utility.Vector3dVector(draw_pointpair)
+        line_set.lines = o3d.utility.Vector2iVector(line_indices)
+        line_set.colors = o3d.utility.Vector3dVector(
+            np.tile(np.array([[0.2, 0.9, 0.2]]), (n_pairs, 1))
+        )
+
+        o3d.visualization.draw_geometries(
+            [mesh_legacy, origin_pcd, hit_pcd, line_set],
+            window_name="Antipodal Point Pairs",
+            mesh_show_back_face=True,
+        )
+
+
+if __name__ == "__main__":
+    mesh_path = "/media/chenjian/_abc/project/grasp_annotator/dustpan_saa.ply"
+    mesh = o3d.t.io.read_triangle_mesh(mesh_path)
+    vertices = torch.from_numpy(mesh.vertex.positions.cpu().numpy())
+    faces = torch.from_numpy(mesh.triangle.indices.cpu().numpy())
+
+    sampler = AntipodalSampler()
+    hit_point_pairs = sampler.sample(vertices, faces)
+    sampler.visualize(hit_point_pairs)
+    print(f"Sampled {hit_point_pairs.shape[0]} antipodal points")
diff --git a/examples/sim/demo/grasp_mug.py b/examples/sim/demo/grasp_mug.py
new file mode 100644
index 00000000..a0a138d0
--- /dev/null
+++ b/examples/sim/demo/grasp_mug.py
@@ -0,0 +1,257 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+"""
+This script demonstrates the creation and simulation of a robot with a soft object,
+and performs a pressing task in a simulated environment.
+"""
+
+import argparse
+import numpy as np
+import time
+import torch
+
+from dexsim.utility.path import get_resources_data_path
+
+from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
+from embodichain.lab.sim.objects import Robot, RigidObject
+from embodichain.lab.sim.utility.action_utils import interpolate_with_distance
+from embodichain.lab.sim.shapes import MeshCfg
+from embodichain.lab.sim.solvers import PytorchSolverCfg
+from embodichain.data import get_data_path
+from embodichain.utils import logger
+from embodichain.lab.sim.cfg import (
+    JointDrivePropertiesCfg,
+    RobotCfg,
+    LightCfg,
+    RigidBodyAttributesCfg,
+    RigidObjectCfg,
+    URDFCfg,
+)
+from embodichain.lab.sim.shapes import MeshCfg
+from embodichain.toolkits.graspkit.pg_grasp.antipodal_annotator import (
+    GraspAnnotatorCfg,
+    AntipodalSamplerCfg,
+)
+
+
+def parse_arguments():
+    """
+    Parse command-line arguments to configure the simulation.
+
+    Returns:
+        argparse.Namespace: Parsed arguments including number of environments and rendering options.
+    """
+    parser = argparse.ArgumentParser(
+        description="Create and simulate a robot in SimulationManager"
+    )
+    parser.add_argument(
+        "--num_envs", type=int, default=1, help="Number of parallel environments"
+    )
+    parser.add_argument(
+        "--enable_rt", action="store_true", help="Enable ray tracing rendering"
+    )
+    parser.add_argument("--headless", action="store_true", help="Enable headless mode")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="cpu",
+        help="device to run the environment on, e.g., 'cpu' or 'cuda'",
+    )
+    return parser.parse_args()
+
+
+def initialize_simulation(args) -> SimulationManager:
+    """
+    Initialize the simulation environment based on the provided arguments.
+
+    Args:
+        args (argparse.Namespace): Parsed command-line arguments.
+
+    Returns:
+        SimulationManager: Configured simulation manager instance.
+    """
+    config = SimulationManagerCfg(
+        headless=True,
+        sim_device=args.device,
+        enable_rt=args.enable_rt,
+        physics_dt=1.0 / 100.0,
+        num_envs=args.num_envs,
+        arena_space=2.5,
+    )
+    sim = SimulationManager(config)
+
+    if args.enable_rt:
+        light = sim.add_light(
+            cfg=LightCfg(
+                uid="main_light",
+                color=(0.6, 0.6, 0.6),
+                intensity=30.0,
+                init_pos=(1.0, 0, 3.0),
+            )
+        )
+
+    return sim
+
+
+def create_robot(sim: SimulationManager, position=[0.0, 0.0, 0.0]) -> Robot:
+    """
+    Create and configure a robot with an arm and a dexterous hand in the simulation.
+
+    Args:
+        sim (SimulationManager): The simulation manager instance.
+
+    Returns:
+        Robot: The configured robot instance added to the simulation.
+    """
+    # Retrieve URDF paths for the robot arm and hand
+    ur10_urdf_path = get_data_path("UniversalRobots/UR10/UR10.urdf")
+    gripper_urdf_path = get_data_path("DH_PGC_140_50_M/DH_PGC_140_50_M.urdf")
+    # Configure the robot with its components and control properties
+    cfg = RobotCfg(
+        uid="UR10",
+        urdf_cfg=URDFCfg(
+            components=[
+                {"component_type": "arm", "urdf_path": ur10_urdf_path},
+                {"component_type": "hand", "urdf_path": gripper_urdf_path},
+            ]
+        ),
+        drive_pros=JointDrivePropertiesCfg(
+            stiffness={"JOINT[0-9]": 1e4, "FINGER[1-2]": 1e3},
+            damping={"JOINT[0-9]": 1e3, "FINGER[1-2]": 1e2},
+            max_effort={"JOINT[0-9]": 1e5, "FINGER[1-2]": 1e4},
+            drive_type="force",
+        ),
+        control_parts={
+            "arm": ["JOINT[0-9]"],
+            "hand": ["FINGER[1-2]"],
+        },
+        solver_cfg={
+            "arm": PytorchSolverCfg(
+                end_link_name="ee_link",
+                root_link_name="base_link",
+                tcp=[
+                    [0.0, 1.0, 0.0, 0.0],
+                    [-1.0, 0.0, 0.0, 0.0],
+                    [0.0, 0.0, 1.0, 0.12],
+                    [0.0, 0.0, 0.0, 1.0],
+                ],
+            )
+        },
+        init_qpos=[0.0, -np.pi / 2, -np.pi / 2, np.pi / 2, -np.pi / 2, 0.0, 0.0, 0.0],
+        init_pos=position,
+    )
+    return sim.add_robot(cfg=cfg)
+
+
+def create_mug(sim: SimulationManager):
+    mug_cfg = RigidObjectCfg(
+        uid="table",
+        shape=MeshCfg(
+            fpath=get_data_path("CoffeeCup/cup.ply"),
+        ),
+        attrs=RigidBodyAttributesCfg(
+            mass=0.01,
+            dynamic_friction=0.97,
+            static_friction=0.99,
+        ),
+        max_convex_hull_num=16,
+        init_pos=[0.55, 0.0, 0.01],
+        init_rot=[0.0, 0.0, -90],
+        body_scale=(4, 4, 4),
+    )
+    mug = sim.add_rigid_object(cfg=mug_cfg)
+    return mug
+
+
+def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tensor):
+    n_envs = sim.num_envs
+    rest_arm_qpos = robot.get_qpos("arm")
+
+    approach_xpos = grasp_xpos.clone()
+    approach_xpos[:, 2, 3] += 0.04
+
+    _, qpos_approach = robot.compute_ik(
+        pose=approach_xpos, joint_seed=rest_arm_qpos, name="arm"
+    )
+    _, qpos_grasp = robot.compute_ik(
+        pose=grasp_xpos, joint_seed=qpos_approach, name="arm"
+    )
+    hand_open_qpos = torch.tensor([0.00, 0.00], dtype=torch.float32, device=sim.device)
+    hand_close_qpos = torch.tensor(
+        [0.025, 0.025], dtype=torch.float32, device=sim.device
+    )
+
+    arm_trajectory = torch.cat(
+        [
+            rest_arm_qpos[:, None, :],
+            qpos_approach[:, None, :],
+            qpos_grasp[:, None, :],
+            qpos_grasp[:, None, :],
+            qpos_approach[:, None, :],
+            rest_arm_qpos[:, None, :],
+        ],
+        dim=1,
+    )
+    hand_trajectory = torch.cat(
+        [
+            hand_open_qpos[None, None, :].repeat(n_envs, 1, 1),
+            hand_open_qpos[None, None, :].repeat(n_envs, 1, 1),
+            hand_open_qpos[None, None, :].repeat(n_envs, 1, 1),
+            hand_close_qpos[None, None, :].repeat(n_envs, 1, 1),
+            hand_close_qpos[None, None, :].repeat(n_envs, 1, 1),
+            hand_close_qpos[None, None, :].repeat(n_envs, 1, 1),
+        ],
+        dim=1,
+    )
+    all_trajectory = torch.cat([arm_trajectory, hand_trajectory], dim=-1)
+    interp_trajectory = interpolate_with_distance(
+        trajectory=all_trajectory, interp_num=300, device=sim.device
+    )
+    return interp_trajectory
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+    sim = initialize_simulation(args)
+    robot = create_robot(sim, position=[0.0, 0.0, 0.0])
+    mug = create_mug(sim)
+
+    # get mug grasp pose
+    grasp_cfg = GraspAnnotatorCfg(
+        viser_port=11801,
+        antipodal_sampler_cfg=AntipodalSamplerCfg(
+            n_sample=5000, max_length=0.088, min_length=0.003
+        ),
+        force_regenerate=True,
+    )
+    sim.open_window()
+    grasp_xpos = mug.get_grasp_pose(
+        approach_direction=torch.tensor(
+            [0, 0, -1], dtype=torch.float32, device=sim.device
+        ),
+        cfg=grasp_cfg,
+        is_visual=True,
+    )
+
+    grab_traj = get_grasp_traj(sim, robot, grasp_xpos)
+    input("Press Enter to start the grab mug demo...")
+    n_waypoint = grab_traj.shape[1]
+    for i in range(n_waypoint):
+        robot.set_qpos(grab_traj[:, i, :])
+        sim.update(step=4)
+        time.sleep(1e-2)
+    input("Press Enter to exit the simulation...")

From 1e15c77b29f491fd3c4e86bb189187b5f92bbc08 Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 23 Mar 2026 19:17:02 +0800
Subject: [PATCH 02/10] update

---
 examples/sim/demo/grasp_mug.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/examples/sim/demo/grasp_mug.py b/examples/sim/demo/grasp_mug.py
index a0a138d0..18c5ff9c 100644
--- a/examples/sim/demo/grasp_mug.py
+++ b/examples/sim/demo/grasp_mug.py
@@ -236,15 +236,19 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
         antipodal_sampler_cfg=AntipodalSamplerCfg(
             n_sample=5000, max_length=0.088, min_length=0.003
         ),
-        force_regenerate=True,
+        force_regenerate=True,  # force user to annotate grasp region each time
     )
     sim.open_window()
+
+    # 1. View grasp object in browser (e.g http://localhost:11801)
+    # 2. press 'Rect Select Region', select grasp region
+    # 3. press 'Confirm Selection' to finish grasp region selection.
     grasp_xpos = mug.get_grasp_pose(
         approach_direction=torch.tensor(
             [0, 0, -1], dtype=torch.float32, device=sim.device
-        ),
+        ),  # gripper approach direction in the mug local frame
         cfg=grasp_cfg,
-        is_visual=True,
+        is_visual=True,  # visualize selected grasp pose finally
     )
 
     grab_traj = get_grasp_traj(sim, robot, grasp_xpos)

From baf731a8ed19e65268dfead52afc3fff93f472bf Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 23 Mar 2026 19:22:12 +0800
Subject: [PATCH 03/10] update comment

---
 examples/sim/demo/grasp_mug.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/sim/demo/grasp_mug.py b/examples/sim/demo/grasp_mug.py
index 18c5ff9c..6ff56d69 100644
--- a/examples/sim/demo/grasp_mug.py
+++ b/examples/sim/demo/grasp_mug.py
@@ -246,7 +246,7 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     grasp_xpos = mug.get_grasp_pose(
         approach_direction=torch.tensor(
             [0, 0, -1], dtype=torch.float32, device=sim.device
-        ),  # gripper approach direction in the mug local frame
+        ),  # gripper approach direction in the world frame
         cfg=grasp_cfg,
         is_visual=True,  # visualize selected grasp pose finally
     )

From f1f043b809c59ff4215ea1fa6d5db3bdd5466281 Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Tue, 24 Mar 2026 11:23:13 +0800
Subject: [PATCH 04/10] add viser dependence

---
 pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 60a12496..25b15290 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,7 +50,8 @@ dependencies = [
   "black==24.3.0",
   "fvcore",
   "h5py",
-  "tensordict"
+  "tensordict",
+  "viser==1.0.21"
 ]
 
 [project.optional-dependencies]

From 63ec5e6667eb0c2419a2a8c47d7d556acd5eb3f0 Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Wed, 25 Mar 2026 19:14:51 +0800
Subject: [PATCH 05/10] update

---
 .../pg_grasp/batch_collision_checker.py       | 528 ++++++++++++++++++
 1 file changed, 528 insertions(+)
 create mode 100644 embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py

diff --git a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
new file mode 100644
index 00000000..f50a12f9
--- /dev/null
+++ b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
@@ -0,0 +1,528 @@
+import trimesh
+import numpy as np
+import torch
+import time
+from typing import List, Tuple, Union
+from dexsim.kit.meshproc import convex_decomposition_coacd
+import hashlib
+from dataclasses import dataclass
+import os
+import pickle
+import open3d as o3d
+from embodichain.utils import logger
+
+
+CONVEX_CACHE_DIR = os.path.join(
+    os.path.expanduser("~"), ".cache", "embodichain_cache", "convex_decomposition"
+)
+
+
+@dataclass
+class BatchConvexCollisionCheckerCfg:
+    collsion_threshold: float = 0.0
+    n_query_mesh_samples: int = 4096
+    debug: bool = False
+
+
+class BatchConvexCollisionChecker:
+    def __init__(
+        self,
+        base_mesh_verts: torch.Tensor,
+        base_mesh_faces: torch.Tensor,
+        max_decomposition_hulls: int = 32,
+    ):
+        if not os.path.isdir(CONVEX_CACHE_DIR):
+            os.makedirs(CONVEX_CACHE_DIR, exist_ok=True)
+        base_mesh_verts_np = base_mesh_verts.cpu().numpy()
+        base_mesh_faces_np = base_mesh_faces.cpu().numpy()
+        mesh_hash = hashlib.md5(
+            (base_mesh_verts_np.tobytes() + base_mesh_faces_np.tobytes())
+        ).hexdigest()
+
+        # for visualization
+        self.mesh = o3d.geometry.TriangleMesh(
+            vertices=o3d.utility.Vector3dVector(base_mesh_verts_np),
+            triangles=o3d.utility.Vector3iVector(base_mesh_faces_np),
+        )
+        self.mesh.compute_vertex_normals()
+        self.cache_path = os.path.join(
+            CONVEX_CACHE_DIR, f"{mesh_hash}_{max_decomposition_hulls}.pkl"
+        )
+
+        if not os.path.isfile(self.cache_path):
+            # generate convex hulls and extract plane equations, then cache to disk
+            self.plane_equations = BatchConvexCollisionChecker._compute_plane_equations(
+                base_mesh_verts_np, base_mesh_faces_np, max_decomposition_hulls
+            )
+            pickle.dump(self.plane_equations, open(self.cache_path, "wb"))
+        else:
+            # load precomputed plane equations from cache
+            self.plane_equations = pickle.load(open(self.cache_path, "rb"))
+
+    def query(
+        self,
+        query_mesh_verts: torch.Tensor,
+        query_mesh_faces: torch.Tensor,
+        poses: torch.Tensor,
+        cfg: BatchConvexCollisionCheckerCfg = BatchConvexCollisionCheckerCfg(),
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        query_mesh = trimesh.Trimesh(
+            vertices=query_mesh_verts.to("cpu").numpy(),
+            faces=query_mesh_faces.to("cpu").numpy(),
+        )
+        n_query = cfg.n_query_mesh_samples
+        n_batch = poses.shape[0]
+        query_points_np = query_mesh.sample(n_query).astype(np.float32)
+        query_points = torch.tensor(
+            query_points_np, device=poses.device
+        )  # [n_query, 3]
+        penetration_result = torch.zeros(size=(n_batch, n_query), device=poses.device)
+        penetration_result.fill_(-float("inf"))
+        collision_result = torch.zeros(
+            size=(n_batch, n_query), dtype=torch.bool, device=poses.device
+        )
+        collision_result.fill_(False)
+        for normals, offsets in self.plane_equations:
+            normals_torch = torch.tensor(normals, device=poses.device)
+            offsets_torch = torch.tensor(offsets, device=poses.device)
+            penetration, collides = check_collision_single_hull(
+                normals_torch,
+                offsets_torch,
+                transform_points_batch(query_points, poses),
+                cfg.collsion_threshold,
+            )
+            penetration_result = torch.max(penetration_result, penetration)
+            collision_result = torch.logical_or(collision_result, collides)
+        is_colliding = collision_result.any(dim=-1)  # [B]
+        max_penetration = penetration_result.max(dim=-1)[0]  # [B]
+
+        if cfg.debug:
+            # visualize result
+            query_points_o3d = o3d.geometry.PointCloud()
+            query_points_o3d.points = o3d.utility.Vector3dVector(query_points_np)
+            query_points_o3d.transform(poses[-1].to("cpu").numpy())
+            query_points_color = np.zeros_like(query_points_np)
+            query_points_color[collision_result[-1].cpu().numpy()] = [
+                1.0,
+                0,
+                0,
+            ]  # red for colliding points
+            query_points_color[~collision_result[-1].cpu().numpy()] = [
+                0,
+                1.0,
+                0,
+            ]  # green for non-colliding points
+            query_points_o3d.colors = o3d.utility.Vector3dVector(query_points_color)
+            o3d.visualization.draw_geometries(
+                [self.mesh, query_points_o3d], mesh_show_back_face=True
+            )
+        return is_colliding, max_penetration
+
+    @staticmethod
+    def _compute_plane_equations(
+        vertices: np.ndarray, faces: np.ndarray, max_decomposition_hulls: int
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Convex decomposition and extract plane equations given mesh vertices and triangles.
+        Each convex hull is represented by its outward-facing face normals and offsets.
+        No padding is applied; each hull can have a different number of faces.
+
+        Args:
+            vertices: [N, 3] vertex positions of the input mesh.
+            faces: [M, 3] triangle indices of the input mesh.
+            max_decomposition_hulls: maximum number of convex hulls to decompose into.
+
+        Returns:
+            List of (normals_i [Ki, 3], offsets_i [Ki]) tuples, one per convex hull.
+            Ki is the number of faces of the i-th hull and can differ across hulls.
+        """
+        mesh = o3d.t.geometry.TriangleMesh()
+        mesh.vertex.positions = o3d.core.Tensor(vertices, dtype=o3d.core.Dtype.Float32)
+        mesh.triangle.indices = o3d.core.Tensor(faces, dtype=o3d.core.Dtype.Int32)
+        is_success, out_mesh_list = convex_decomposition_coacd(
+            mesh, max_convex_hull_num=max_decomposition_hulls
+        )
+        convex_vert_face_list = []
+        for out_mesh in out_mesh_list:
+            verts = out_mesh.vertex.positions.numpy()
+            faces = out_mesh.triangle.indices.numpy()
+            convex_vert_face_list.append((verts, faces))
+        return extract_plane_equations(convex_vert_face_list)
+
+
+def extract_plane_equations(
+    convex_meshes: List[Tuple[np.ndarray, np.ndarray]],
+) -> List[Tuple[np.ndarray, np.ndarray]]:
+    """
+    Extract plane equations from a list of convex hull meshes.
+    Each convex hull is represented by its outward-facing face normals and offsets.
+    No padding is applied; each hull can have a different number of faces.
+
+    Args:
+        convex_meshes: List of convex hull data.
+            - tuple of (vertices [N,3], faces [M,3])
+
+    Returns:
+        List of (normals_i [Ki, 3], offsets_i [Ki]) tuples, one per convex hull.
+        Ki is the number of faces of the i-th hull and can differ across hulls.
+    """
+    convex_plane_data = []
+
+    for i, convex_mesh_data in enumerate(convex_meshes):
+        vertices, faces = convex_mesh_data
+        hull = trimesh.Trimesh(
+            vertices=vertices,
+            faces=faces,
+        )
+        # Outward-facing face normals [Ki, 3]
+        face_normals = hull.face_normals
+        # One vertex per face to compute offset [Ki, 3]
+        face_origins = hull.triangles[:, 0, :]
+        # Plane equation: n · x + d = 0  =>  d = -(n · p)
+        offsets_i = -np.sum(face_normals * face_origins, axis=1)
+
+        convex_plane_data.append(
+            (face_normals.astype(np.float32), offsets_i.astype(np.float32))
+        )
+    return convex_plane_data
+
+
+def sample_surface_points(mesh_path: str, num_points: int = 4096) -> np.ndarray:
+    """
+    Sample surface points from a mesh file.
+
+    Args:
+        mesh_path: Path to the mesh file.
+        num_points: Number of surface points to sample.
+
+    Returns:
+        points: [P, 3] numpy array of sampled surface points.
+    """
+    mesh = trimesh.load(mesh_path, force="mesh")
+    points = mesh.sample(num_points)
+    return points.astype(np.float32)
+
+
+def check_collision_single_hull(
+    normals: torch.Tensor,  # [K, 3]
+    offsets: torch.Tensor,  # [K]
+    transformed_points: torch.Tensor,  # [B, P, 3]
+    threshold: float = 0.0,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Check collision between a batch of transformed point clouds and a single convex hull.
+
+    A point p is inside the convex hull iff:
+        max_k (n_k · p + d_k) <= 0
+
+    Penetration depth for a point is defined as:
+        penetration = -(max_k (n_k · p + d_k))
+    Positive penetration means the point is inside the hull.
+
+    Args:
+        normals: [K, 3] outward face normals of the convex hull.
+        offsets: [K] plane offsets of the convex hull.
+        transformed_points: [B, P, 3] point cloud already transformed by batch poses.
+        threshold: collision threshold. A point is considered colliding if
+                   its signed distance to the hull interior is <= threshold.
+
+    Returns:
+        penetration: [B, P] penetration depth for each point.
+                     Positive values indicate the point is inside the hull.
+        collides: [B, P] boolean mask, True if the point collides with this hull.
+    """
+    # signed_dist: [B, P, K] = einsum([B,P,3], [K,3]) + [K]
+    signed_dist = torch.einsum("bpj, kj -> bpk", transformed_points, normals) + offsets
+
+    # For each point, the maximum signed distance across all planes
+    # If max <= 0, the point satisfies all half-plane constraints => inside the hull
+    max_over_planes, _ = signed_dist.max(dim=-1)  # [B, P]
+
+    # Penetration depth: negate so that positive = inside
+    penetration = -max_over_planes  # [B, P]
+
+    # A point collides if its penetration exceeds the threshold
+    collides = penetration > threshold  # [B, P]
+
+    return penetration, collides
+
+
+def merge_collision_results(
+    hull_results: List[Tuple[torch.Tensor, torch.Tensor]], device: torch.device
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Merge collision detection results from multiple convex hulls.
+
+    A pose is considered colliding if ANY point penetrates ANY convex hull.
+    The reported penetration depth is the maximum across all points and all hulls.
+
+    Args:
+        hull_results: List of (penetration [B, P], collides [B, P]) tuples,
+                      one per convex hull, as returned by check_collision_single_hull.
+        device: torch device.
+
+    Returns:
+        overall_collisions: [B] boolean, True if the pose collides with any hull.
+        overall_max_penetrations: [B] float, maximum penetration depth per pose.
+    """
+    if not hull_results:
+        raise ValueError("hull_results is empty, nothing to merge.")
+
+    B = hull_results[0][0].shape[0]
+
+    overall_collisions = torch.zeros(B, dtype=torch.bool, device=device)
+    overall_max_penetrations = torch.full(
+        (B,), -float("inf"), dtype=torch.float32, device=device
+    )
+
+    for penetration, collides in hull_results:
+        # Update collision flag: OR across hulls
+        # A pose collides if any point collides with this hull
+        overall_collisions |= collides.any(dim=-1)  # [B]
+
+        # Update max penetration: take the per-pose maximum across all points for this hull,
+        # then compare with the running maximum
+        hull_max_pen = penetration.max(dim=-1)[0]  # [B]
+        overall_max_penetrations = torch.max(overall_max_penetrations, hull_max_pen)
+
+    return overall_collisions, overall_max_penetrations
+
+
+def transform_points_batch(
+    points: torch.Tensor, poses: torch.Tensor  # [P, 3]  # [B, 4, 4]
+) -> torch.Tensor:
+    """
+    Apply a batch of rigid transforms to a point cloud.
+
+    Args:
+        points: [P, 3] source point cloud.
+        poses: [B, 4, 4] batch of homogeneous transformation matrices.
+
+    Returns:
+        transformed: [B, P, 3] transformed point cloud for each pose.
+    """
+    R = poses[:, :3, :3]  # [B, 3, 3]
+    t = poses[:, :3, 3]  # [B, 3]
+    transformed = torch.einsum("bij, pj -> bpi", R, points) + t.unsqueeze(1)
+    return transformed
+
+
+def batch_collision_detection(
+    convex_planes: List[Tuple[torch.Tensor, torch.Tensor]],
+    points_B: torch.Tensor,  # [P, 3]
+    poses: torch.Tensor,  # [B, 4, 4]
+    threshold: float = 0.0,
+    chunk_size: int = 512,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Full batch collision detection pipeline.
+
+    Iterates over convex hulls sequentially and over pose chunks to control
+    GPU memory usage. Within each (hull, chunk) pair, the computation is
+    fully parallelized over B_chunk * P * K.
+
+    Args:
+        convex_planes: List of (normals [Ki, 3], offsets [Ki]) tensors on device,
+                       one per convex hull. Ki can differ across hulls.
+        points_B: [P, 3] sampled surface points of mesh B, on device.
+        poses: [B, 4, 4] batch of relative poses, on device.
+        threshold: collision threshold (positive = safety margin).
+        chunk_size: number of poses to process per chunk.
+
+    Returns:
+        overall_collisions: [B] bool
+        overall_max_penetrations: [B] float
+    """
+    device = points_B.device
+    B = poses.shape[0]
+
+    all_hull_results: List[Tuple[torch.Tensor, torch.Tensor]] = []
+
+    # Sequential iteration over convex hulls to save memory
+    for hull_idx, (normals, offsets) in enumerate(convex_planes):
+        hull_pen_chunks = []
+        hull_col_chunks = []
+
+        # Chunk over batch dimension to control peak memory
+        for start in range(0, B, chunk_size):
+            end = min(start + chunk_size, B)
+            poses_chunk = poses[start:end]
+
+            # Transform points for this chunk of poses
+            transformed_chunk = transform_points_batch(
+                points_B, poses_chunk
+            )  # [B_chunk, P, 3]
+
+            # Check collision against this single hull
+            penetration, collides = check_collision_single_hull(
+                normals, offsets, transformed_chunk, threshold
+            )
+
+            hull_pen_chunks.append(penetration)
+            hull_col_chunks.append(collides)
+
+        # Concatenate chunks for this hull
+        hull_penetration = torch.cat(hull_pen_chunks, dim=0)  # [B, P]
+        hull_collides = torch.cat(hull_col_chunks, dim=0)  # [B, P]
+
+        all_hull_results.append((hull_penetration, hull_collides))
+
+    # Merge results across all hulls
+    overall_collisions, overall_max_penetrations = merge_collision_results(
+        all_hull_results, device
+    )
+
+    return overall_collisions, overall_max_penetrations
+
+
+def main():
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+
+    # --- Create dummy mesh files for testing ---
+    box1 = trimesh.primitives.Box(extents=[0.5, 0.5, 0.5])
+    box2 = trimesh.primitives.Box(
+        extents=[0.4, 0.4, 0.4],
+        transform=trimesh.transformations.translation_matrix([1, 0, 0]),
+    )
+    box1.export("mesh_hull_1.obj")
+    box2.export("mesh_hull_2.obj")
+
+    sphere_mesh = trimesh.primitives.Sphere(radius=0.3)
+    sphere_mesh.export("mesh_B.obj")
+    print("Created dummy mesh files.\n")
+
+    # ==================== Preprocessing ====================
+
+    # Load externally decomposed convex hull meshes
+    convex_mesh_files = ["mesh_hull_1.obj", "mesh_hull_2.obj"]
+    convex_meshes = load_convex_meshes(convex_mesh_files)
+    if not convex_meshes:
+        print("No convex hulls loaded. Exiting.")
+        return
+
+    # Extract plane equations (list of variable-length arrays)
+    convex_plane_data_np = extract_plane_equations(convex_meshes)
+
+    # Convert to torch tensors on device
+    convex_planes_torch: List[Tuple[torch.Tensor, torch.Tensor]] = []
+    for normals_np, offsets_np in convex_plane_data_np:
+        convex_planes_torch.append(
+            (
+                torch.tensor(normals_np, device=device),  # [Ki, 3]
+                torch.tensor(offsets_np, device=device),  # [Ki]
+            )
+        )
+
+    # Sample surface points from mesh B
+    points_np = sample_surface_points("mesh_B.obj", num_points=2048)
+    points_B = torch.tensor(points_np, device=device)  # [P, 3]
+
+    # ==================== Generate test poses ====================
+    B = 10000
+    chunk_size = 1024
+
+    # Random rotation matrices via SVD
+    random_mat = torch.randn(B, 3, 3, device=device)
+    U, _, Vt = torch.linalg.svd(random_mat)
+    R = U @ Vt
+    # Fix reflections to ensure proper rotations (det = +1)
+    det = torch.det(R)
+    R[det < 0] *= -1
+
+    poses = torch.eye(4, device=device).unsqueeze(0).repeat(B, 1, 1)
+    poses[:, :3, :3] = R
+    poses[:, :3, 3] = torch.randn(B, 3, device=device) * 0.5
+
+    # ==================== Run collision detection ====================
+    print(
+        f"\nRunning collision detection: {B} poses, {points_B.shape[0]} points, "
+        f"{len(convex_planes_torch)} hulls..."
+    )
+
+    torch.cuda.synchronize() if device.type == "cuda" else None
+    start_time = time.time()
+
+    with torch.no_grad():
+        collisions, penetration_depths = batch_collision_detection(
+            convex_planes_torch, points_B, poses, threshold=0.001, chunk_size=chunk_size
+        )
+
+    torch.cuda.synchronize() if device.type == "cuda" else None
+    elapsed = time.time() - start_time
+
+    # ==================== Report results ====================
+    print(f"\n{'='*40}")
+    print(f"Total poses:       {B}")
+    print(f"Collisions:        {collisions.sum().item()} / {B}")
+    if collisions.any():
+        print(f"Max penetration:   {penetration_depths[collisions].max().item():.6f}")
+    else:
+        print(f"Max penetration:   N/A (no collisions)")
+    print(f"Total time:        {elapsed:.3f}s")
+    print(f"Per pose:          {elapsed / B * 1e6:.2f} μs")
+    print(f"{'='*40}")
+
+    # ==================== Benchmark ====================
+    num_iters = 50
+    torch.cuda.synchronize() if device.type == "cuda" else None
+    t0 = time.time()
+    for _ in range(num_iters):
+        with torch.no_grad():
+            batch_collision_detection(
+                convex_planes_torch,
+                points_B,
+                poses,
+                threshold=0.001,
+                chunk_size=chunk_size,
+            )
+    torch.cuda.synchronize() if device.type == "cuda" else None
+    t1 = time.time()
+
+    avg_ms = (t1 - t0) / num_iters * 1000
+    print(
+        f"\nBenchmark ({num_iters} iters): {avg_ms:.2f} ms/iter, "
+        f"{avg_ms / B * 1000:.2f} μs/pose"
+    )
+
+
+if __name__ == "__main__":
+    from embodichain.data import get_data_path
+
+    bottle_a_path = get_data_path("ScannedBottle/moliwulong_processed.ply")
+    bottle_b_path = get_data_path("ScannedBottle/yibao_processed.ply")
+
+    bottle_a_mesh = trimesh.load(bottle_a_path)
+    bottle_b_mesh = trimesh.load(bottle_b_path)
+    bottle_a_verts = torch.tensor(bottle_a_mesh.vertices, dtype=torch.float32)
+    bottle_a_faces = torch.tensor(bottle_a_mesh.faces, dtype=torch.int64)
+    bottle_b_verts = torch.tensor(bottle_b_mesh.vertices, dtype=torch.float32)
+    bottle_b_faces = torch.tensor(bottle_b_mesh.faces, dtype=torch.int64)
+
+    collision_checker = BatchConvexCollisionChecker(bottle_a_verts, bottle_a_faces)
+    poses = torch.tensor(
+        [
+            [
+                [1, 0, 0, 0],
+                [0, 1, 0, 0],
+                [0, 0, 1, 1.0],
+                [0, 0, 0, 1],
+            ],
+            [
+                [1, 0, 0, 0.05],
+                [0, -1, 0, 0],
+                [0, 0, -1, 0],
+                [0, 0, 0, 1],
+            ],
+        ]
+    )
+    check_cfg = BatchConvexCollisionCheckerCfg(
+        debug=False,
+        n_query_mesh_samples=32768,
+        collsion_threshold=-0.003,
+    )
+    collisions, penetrations = collision_checker.query(
+        bottle_b_verts, bottle_b_faces, poses, cfg=check_cfg
+    )
+    print("Collisions:", collisions)
+    print("Penetrations:", penetrations)

From 73781d887eb82142224cc35daedc25ad505e964b Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Thu, 26 Mar 2026 17:40:42 +0800
Subject: [PATCH 06/10] update

---
 .../graspkit/pg_grasp/antipodal_annotator.py  |  16 ++
 .../graspkit/pg_grasp/antipodal_sampler.py    |  16 ++
 .../pg_grasp/batch_collision_checker.py       | 179 ------------------
 .../pg_grasp/gripper_collision_checker.py     |   0
 4 files changed, 32 insertions(+), 179 deletions(-)
 create mode 100644 embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py

diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
index 4852879e..5ee3eda4 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
@@ -1,3 +1,19 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
 import os
 import argparse
 import open3d as o3d
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
index 1eb3ec61..09e4858e 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
@@ -1,3 +1,19 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
 import torch
 import torch.nn.functional as F
 import numpy as np
diff --git a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
index f50a12f9..cf18b76e 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
@@ -307,185 +307,6 @@ def transform_points_batch(
     return transformed
 
 
-def batch_collision_detection(
-    convex_planes: List[Tuple[torch.Tensor, torch.Tensor]],
-    points_B: torch.Tensor,  # [P, 3]
-    poses: torch.Tensor,  # [B, 4, 4]
-    threshold: float = 0.0,
-    chunk_size: int = 512,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Full batch collision detection pipeline.
-
-    Iterates over convex hulls sequentially and over pose chunks to control
-    GPU memory usage. Within each (hull, chunk) pair, the computation is
-    fully parallelized over B_chunk * P * K.
-
-    Args:
-        convex_planes: List of (normals [Ki, 3], offsets [Ki]) tensors on device,
-                       one per convex hull. Ki can differ across hulls.
-        points_B: [P, 3] sampled surface points of mesh B, on device.
-        poses: [B, 4, 4] batch of relative poses, on device.
-        threshold: collision threshold (positive = safety margin).
-        chunk_size: number of poses to process per chunk.
-
-    Returns:
-        overall_collisions: [B] bool
-        overall_max_penetrations: [B] float
-    """
-    device = points_B.device
-    B = poses.shape[0]
-
-    all_hull_results: List[Tuple[torch.Tensor, torch.Tensor]] = []
-
-    # Sequential iteration over convex hulls to save memory
-    for hull_idx, (normals, offsets) in enumerate(convex_planes):
-        hull_pen_chunks = []
-        hull_col_chunks = []
-
-        # Chunk over batch dimension to control peak memory
-        for start in range(0, B, chunk_size):
-            end = min(start + chunk_size, B)
-            poses_chunk = poses[start:end]
-
-            # Transform points for this chunk of poses
-            transformed_chunk = transform_points_batch(
-                points_B, poses_chunk
-            )  # [B_chunk, P, 3]
-
-            # Check collision against this single hull
-            penetration, collides = check_collision_single_hull(
-                normals, offsets, transformed_chunk, threshold
-            )
-
-            hull_pen_chunks.append(penetration)
-            hull_col_chunks.append(collides)
-
-        # Concatenate chunks for this hull
-        hull_penetration = torch.cat(hull_pen_chunks, dim=0)  # [B, P]
-        hull_collides = torch.cat(hull_col_chunks, dim=0)  # [B, P]
-
-        all_hull_results.append((hull_penetration, hull_collides))
-
-    # Merge results across all hulls
-    overall_collisions, overall_max_penetrations = merge_collision_results(
-        all_hull_results, device
-    )
-
-    return overall_collisions, overall_max_penetrations
-
-
-def main():
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    print(f"Using device: {device}")
-
-    # --- Create dummy mesh files for testing ---
-    box1 = trimesh.primitives.Box(extents=[0.5, 0.5, 0.5])
-    box2 = trimesh.primitives.Box(
-        extents=[0.4, 0.4, 0.4],
-        transform=trimesh.transformations.translation_matrix([1, 0, 0]),
-    )
-    box1.export("mesh_hull_1.obj")
-    box2.export("mesh_hull_2.obj")
-
-    sphere_mesh = trimesh.primitives.Sphere(radius=0.3)
-    sphere_mesh.export("mesh_B.obj")
-    print("Created dummy mesh files.\n")
-
-    # ==================== Preprocessing ====================
-
-    # Load externally decomposed convex hull meshes
-    convex_mesh_files = ["mesh_hull_1.obj", "mesh_hull_2.obj"]
-    convex_meshes = load_convex_meshes(convex_mesh_files)
-    if not convex_meshes:
-        print("No convex hulls loaded. Exiting.")
-        return
-
-    # Extract plane equations (list of variable-length arrays)
-    convex_plane_data_np = extract_plane_equations(convex_meshes)
-
-    # Convert to torch tensors on device
-    convex_planes_torch: List[Tuple[torch.Tensor, torch.Tensor]] = []
-    for normals_np, offsets_np in convex_plane_data_np:
-        convex_planes_torch.append(
-            (
-                torch.tensor(normals_np, device=device),  # [Ki, 3]
-                torch.tensor(offsets_np, device=device),  # [Ki]
-            )
-        )
-
-    # Sample surface points from mesh B
-    points_np = sample_surface_points("mesh_B.obj", num_points=2048)
-    points_B = torch.tensor(points_np, device=device)  # [P, 3]
-
-    # ==================== Generate test poses ====================
-    B = 10000
-    chunk_size = 1024
-
-    # Random rotation matrices via SVD
-    random_mat = torch.randn(B, 3, 3, device=device)
-    U, _, Vt = torch.linalg.svd(random_mat)
-    R = U @ Vt
-    # Fix reflections to ensure proper rotations (det = +1)
-    det = torch.det(R)
-    R[det < 0] *= -1
-
-    poses = torch.eye(4, device=device).unsqueeze(0).repeat(B, 1, 1)
-    poses[:, :3, :3] = R
-    poses[:, :3, 3] = torch.randn(B, 3, device=device) * 0.5
-
-    # ==================== Run collision detection ====================
-    print(
-        f"\nRunning collision detection: {B} poses, {points_B.shape[0]} points, "
-        f"{len(convex_planes_torch)} hulls..."
-    )
-
-    torch.cuda.synchronize() if device.type == "cuda" else None
-    start_time = time.time()
-
-    with torch.no_grad():
-        collisions, penetration_depths = batch_collision_detection(
-            convex_planes_torch, points_B, poses, threshold=0.001, chunk_size=chunk_size
-        )
-
-    torch.cuda.synchronize() if device.type == "cuda" else None
-    elapsed = time.time() - start_time
-
-    # ==================== Report results ====================
-    print(f"\n{'='*40}")
-    print(f"Total poses:       {B}")
-    print(f"Collisions:        {collisions.sum().item()} / {B}")
-    if collisions.any():
-        print(f"Max penetration:   {penetration_depths[collisions].max().item():.6f}")
-    else:
-        print(f"Max penetration:   N/A (no collisions)")
-    print(f"Total time:        {elapsed:.3f}s")
-    print(f"Per pose:          {elapsed / B * 1e6:.2f} μs")
-    print(f"{'='*40}")
-
-    # ==================== Benchmark ====================
-    num_iters = 50
-    torch.cuda.synchronize() if device.type == "cuda" else None
-    t0 = time.time()
-    for _ in range(num_iters):
-        with torch.no_grad():
-            batch_collision_detection(
-                convex_planes_torch,
-                points_B,
-                poses,
-                threshold=0.001,
-                chunk_size=chunk_size,
-            )
-    torch.cuda.synchronize() if device.type == "cuda" else None
-    t1 = time.time()
-
-    avg_ms = (t1 - t0) / num_iters * 1000
-    print(
-        f"\nBenchmark ({num_iters} iters): {avg_ms:.2f} ms/iter, "
-        f"{avg_ms / B * 1000:.2f} μs/pose"
-    )
-
-
 if __name__ == "__main__":
     from embodichain.data import get_data_path
 
diff --git a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
new file mode 100644
index 00000000..e69de29b

From e0d129d6a7dfce09259e674ec262615370479c43 Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 30 Mar 2026 10:39:45 +0800
Subject: [PATCH 07/10] TODO: too slow

---
 embodichain/lab/sim/objects/rigid_object.py   |  18 +-
 .../graspkit/pg_grasp/antipodal_annotator.py  | 126 +++++++---
 .../pg_grasp/batch_collision_checker.py       | 141 ++++++++---
 .../pg_grasp/gripper_collision_checker.py     | 230 ++++++++++++++++++
 examples/sim/demo/grasp_mug.py                |   2 +-
 5 files changed, 431 insertions(+), 86 deletions(-)

diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index 2e058ad3..4da6e632 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -1140,8 +1140,6 @@ def get_grasp_pose(
             )
         approach_direction = F.normalize(approach_direction, dim=-1)
         if hasattr(self, "_grasp_annotator") is False:
-            self._grasp_annotator = GraspAnnotator(cfg=cfg)
-        if hasattr(self, "_hit_point_pairs") is False or cfg.force_regenerate:
             vertices = torch.tensor(
                 self._entities[0].get_vertices(),
                 dtype=torch.float32,
@@ -1156,7 +1154,13 @@ def get_grasp_pose(
                 device=self.device,
             )
             vertices = vertices * scale
-            self._hit_point_pairs = self._grasp_annotator.annotate(vertices, triangles)
+            self._grasp_annotator = GraspAnnotator(
+                vertices=vertices, triangles=triangles, cfg=cfg
+            )
+
+        # Annotate antipodal point pairs
+        if hasattr(self, "_hit_point_pairs") is False or cfg.force_regenerate:
+            self._hit_point_pairs = self._grasp_annotator.annotate()
 
         poses = self.get_local_pose(to_matrix=True)
         poses = torch.as_tensor(poses, dtype=torch.float32, device=self.device)
@@ -1177,13 +1181,7 @@ def get_grasp_pose(
             triangles = self._entities[0].get_triangles()
             scale = self._entities[0].get_body_scale()
             vertices = vertices * scale
-            GraspAnnotator.visualize_grasp_pose(
-                vertices=torch.tensor(
-                    vertices, dtype=torch.float32, device=self.device
-                ),
-                triangles=torch.tensor(
-                    triangles, dtype=torch.int32, device=self.device
-                ),
+            self._grasp_annotator.visualize_grasp_pose(
                 obj_pose=poses[0],
                 grasp_pose=grasp_poses[0],
                 open_length=open_lengths[0],
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
index 5ee3eda4..bf84a9f3 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
@@ -32,6 +32,10 @@
     AntipodalSampler,
     AntipodalSamplerCfg,
 )
+from .gripper_collision_checker import (
+    SimpleGripperCollisionChecker,
+    SimpleGripperCollisionCfg,
+)
 import hashlib
 import torch.nn.functional as F
 import tempfile
@@ -55,18 +59,37 @@ class SelectResult:
 
 
 class GraspAnnotator:
-    def __init__(self, cfg: GraspAnnotatorCfg = GraspAnnotatorCfg()) -> None:
+    def __init__(
+        self,
+        vertices: torch.Tensor,
+        triangles: torch.Tensor,
+        cfg: GraspAnnotatorCfg = GraspAnnotatorCfg(),
+    ) -> None:
+        self.device = vertices.device
+        self.vertices = vertices
+        self.triangles = triangles
+        self.mesh = trimesh.Trimesh(
+            vertices=vertices.to("cpu").numpy(),
+            faces=triangles.to("cpu").numpy(),
+            process=False,
+            force="mesh",
+        )
+        self._collision_checker = SimpleGripperCollisionChecker(
+            object_mesh_verts=vertices,
+            object_mesh_faces=triangles,
+            cfg=SimpleGripperCollisionCfg(),
+        )
         self.cfg = cfg
         self.antipodal_sampler = AntipodalSampler(cfg=cfg.antipodal_sampler_cfg)
 
-    def annotate(self, vertices: torch.Tensor, triangles: torch.Tensor):
-        cache_path = self._get_cache_dir(vertices, triangles)
+    def annotate(self):
+        cache_path = self._get_cache_dir(self.vertices, self.triangles)
         if os.path.exists(cache_path) and not self.cfg.force_regenerate:
             logger.log_info(
                 f"Found existing antipodal retult. Loading cached antipodal pairs from {cache_path}"
             )
             hit_point_pairs = torch.tensor(
-                np.load(cache_path), dtype=torch.float32, device=vertices.device
+                np.load(cache_path), dtype=torch.float32, device=self.device
             )
             return hit_point_pairs
         else:
@@ -74,14 +97,6 @@ def annotate(self, vertices: torch.Tensor, triangles: torch.Tensor):
                 f"[Viser] *****Annotate grasp region in http://localhost:{self.cfg.viser_port}"
             )
 
-        self.mesh = trimesh.Trimesh(
-            vertices=vertices.to("cpu").numpy(),
-            faces=triangles.to("cpu").numpy(),
-            process=False,
-            force="mesh",
-        )
-        self.device = vertices.device
-
         server = viser.ViserServer(port=self.cfg.viser_port)
         server.gui.configure_theme(brand_color=(130, 0, 150))
         server.scene.set_up_direction("+z")
@@ -362,56 +377,93 @@ def get_approach_grasp_poses(
         """
         origin_points = hit_point_pairs[:, 0, :]
         hit_points = hit_point_pairs[:, 1, :]
-        print("origin_points dtype:", origin_points.dtype)
-        print("object_pose dtype:", object_pose.dtype)
         origin_points_ = self._apply_transform(origin_points, object_pose)
         hit_points_ = self._apply_transform(hit_points, object_pose)
         centers = (origin_points_ + hit_points_) / 2
         center = centers.mean(dim=0)
 
-        # get best grasp pose
+        # filter perpendicular antipodal point
         grasp_x = F.normalize(hit_points_ - origin_points_, dim=-1)
         cos_angle = torch.clamp((grasp_x * approach_direction).sum(dim=-1), -1.0, 1.0)
         positive_angle = torch.abs(torch.acos(cos_angle))
-        antipodal_length = torch.norm(hit_points_ - origin_points_, dim=-1)
-        length_cost = 1 - antipodal_length / antipodal_length.max()
+        valid_mask = (
+            positive_angle - torch.pi / 2
+        ).abs() <= self.cfg.max_deviation_angle
+        valid_grasp_x = grasp_x[valid_mask]
+        valid_centers = centers[valid_mask]
+
+        # compute grasp poses using antipodal point pairs and approach direction
+        valid_grasp_poses = GraspAnnotator._grasp_pose_from_approach_direction(
+            valid_grasp_x, approach_direction, valid_centers
+        )
+        valid_open_lengths = torch.norm(
+            origin_points_[valid_mask] - hit_points_[valid_mask], dim=-1
+        )
+        # select non-collide grasp poses
+
+        is_colliding, max_penetration = self._collision_checker.query(
+            object_pose, valid_grasp_poses, valid_open_lengths
+        )
+
+        # get best grasp pose
+        valid_grasp_poses = valid_grasp_poses[~is_colliding]
+        valid_open_lengths = valid_open_lengths[~is_colliding]
+        valid_centers = valid_centers[~is_colliding]
+        valid_grasp_x = F.normalize(valid_grasp_poses[:, :3, 0], dim=-1)
+
+        cos_angle = torch.clamp(
+            (valid_grasp_x * approach_direction).sum(dim=-1), -1.0, 1.0
+        )
+        positive_angle = torch.abs(torch.acos(cos_angle))
         angle_cost = torch.abs(positive_angle - 0.5 * torch.pi) / (0.5 * torch.pi)
-        center_distance = torch.norm(centers - center, dim=-1)
+        center_distance = torch.norm(valid_centers - center, dim=-1)
         center_cost = center_distance / center_distance.max()
+        length_cost = 1 - valid_open_lengths / valid_open_lengths.max()
         total_cost = 0.4 * angle_cost + 0.3 * length_cost + 0.3 * center_cost
         best_idx = torch.argmin(total_cost)
-
-        best_open_length = torch.norm(hit_points_[best_idx] - origin_points_[best_idx])
-        best_grasp_x = grasp_x[best_idx]
-        best_grasp_center = centers[best_idx]
-        best_grasp_y = torch.cross(approach_direction, best_grasp_x, dim=0)
-        best_grasp_y = F.normalize(best_grasp_y, dim=-1)
-        best_grasp_z = torch.cross(best_grasp_x, best_grasp_y, dim=0)
-        best_grasp_z = F.normalize(best_grasp_z, dim=-1)
-        grasp_pose = torch.eye(4, device=hit_point_pairs.device, dtype=torch.float32)
-        grasp_pose[:3, 0] = best_grasp_x
-        grasp_pose[:3, 1] = best_grasp_y
-        grasp_pose[:3, 2] = best_grasp_z
-        grasp_pose[:3, 3] = best_grasp_center
-        return grasp_pose, best_open_length
+        best_grasp_pose = valid_grasp_poses[best_idx]
+        best_open_length = valid_open_lengths[best_idx]
+        return best_grasp_pose, best_open_length
 
     @staticmethod
+    def _grasp_pose_from_approach_direction(
+        grasp_x: torch.Tensor, approach_direction: torch.Tensor, center: torch.Tensor
+    ):
+        approach_direction_repeat = approach_direction[None, :].repeat(
+            grasp_x.shape[0], 1
+        )
+        grasp_y = torch.cross(approach_direction_repeat, grasp_x, dim=-1)
+        grasp_y = F.normalize(grasp_y, dim=-1)
+        grasp_z = torch.cross(grasp_x, grasp_y, dim=-1)
+        grasp_z = F.normalize(grasp_z, dim=-1)
+        grasp_poses = (
+            torch.eye(4, device=grasp_x.device, dtype=torch.float32)
+            .unsqueeze(0)
+            .repeat(grasp_x.shape[0], 1, 1)
+        )
+        grasp_poses[:, :3, 0] = grasp_x
+        grasp_poses[:, :3, 1] = grasp_y
+        grasp_poses[:, :3, 2] = grasp_z
+        grasp_poses[:, :3, 3] = center
+        return grasp_poses
+
     def visualize_grasp_pose(
-        vertices: torch.Tensor,
-        triangles: torch.Tensor,
+        self,
         obj_pose: torch.Tensor,
         grasp_pose: torch.Tensor,
         open_length: float,
     ):
         mesh = o3d.geometry.TriangleMesh(
-            vertices=o3d.utility.Vector3dVector(vertices.to("cpu").numpy()),
-            triangles=o3d.utility.Vector3iVector(triangles.to("cpu").numpy()),
+            vertices=o3d.utility.Vector3dVector(self.vertices.to("cpu").numpy()),
+            triangles=o3d.utility.Vector3iVector(self.triangles.to("cpu").numpy()),
         )
         mesh.compute_vertex_normals()
         mesh.paint_uniform_color([0.3, 0.6, 0.3])
         mesh.transform(obj_pose.to("cpu").numpy())
         vertices_ = torch.tensor(
-            np.asarray(mesh.vertices), device=vertices.device, dtype=vertices.dtype
+            np.asarray(mesh.vertices),
+            device=self.vertices.device,
+            dtype=self.vertices.dtype,
         )
         mesh_scale = (vertices_.max(dim=0)[0] - vertices_.min(dim=0)[0]).max().item()
         groud_plane = o3d.geometry.TriangleMesh.create_cylinder(
diff --git a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
index cf18b76e..25327bea 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
@@ -11,7 +11,6 @@
 import open3d as o3d
 from embodichain.utils import logger
 
-
 CONVEX_CACHE_DIR = os.path.join(
     os.path.expanduser("~"), ".cache", "embodichain_cache", "convex_decomposition"
 )
@@ -33,6 +32,7 @@ def __init__(
     ):
         if not os.path.isdir(CONVEX_CACHE_DIR):
             os.makedirs(CONVEX_CACHE_DIR, exist_ok=True)
+        self.device = base_mesh_verts.device
         base_mesh_verts_np = base_mesh_verts.cpu().numpy()
         base_mesh_faces_np = base_mesh_faces.cpu().numpy()
         mesh_hash = hashlib.md5(
@@ -45,6 +45,7 @@ def __init__(
             triangles=o3d.utility.Vector3iVector(base_mesh_faces_np),
         )
         self.mesh.compute_vertex_normals()
+
         self.cache_path = os.path.join(
             CONVEX_CACHE_DIR, f"{mesh_hash}_{max_decomposition_hulls}.pkl"
         )
@@ -59,6 +60,58 @@ def __init__(
             # load precomputed plane equations from cache
             self.plane_equations = pickle.load(open(self.cache_path, "rb"))
 
+    def query_batch_points(
+        self,
+        batch_points: torch.Tensor,
+        collision_threshold: float = 0.0,
+        is_visual: bool = False,
+    ):
+        n_batch = batch_points.shape[0]
+        n_points = batch_points.shape[1]
+        penetration_result = torch.zeros(size=(n_batch, n_points), device=self.device)
+        penetration_result.fill_(-float("inf"))
+        collision_result = torch.zeros(
+            size=(n_batch, n_points), dtype=torch.bool, device=self.device
+        )
+        collision_result.fill_(False)
+        for normals, offsets in self.plane_equations:
+            normals_torch = torch.tensor(normals, device=self.device)
+            offsets_torch = torch.tensor(offsets, device=self.device)
+            penetration, collides = check_collision_single_hull(
+                normals_torch,
+                offsets_torch,
+                batch_points,
+                collision_threshold,
+            )
+            penetration_result = torch.max(penetration_result, penetration)
+            collision_result = torch.logical_or(collision_result, collides)
+        is_colliding = collision_result.any(dim=-1)  # [B]
+        max_penetration = penetration_result.max(dim=-1)[0]  # [B]
+
+        if is_visual:
+            # visualize result
+            frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1)
+            for i in range(n_batch):
+                query_points_o3d = o3d.geometry.PointCloud()
+                query_points_np = batch_points[i].cpu().numpy()
+                query_points_o3d.points = o3d.utility.Vector3dVector(query_points_np)
+                query_points_color = np.zeros_like(query_points_np)
+                query_points_color[collision_result[i].cpu().numpy()] = [
+                    1.0,
+                    0,
+                    0,
+                ]  # red for colliding points
+                query_points_color[~collision_result[i].cpu().numpy()] = [
+                    0,
+                    1.0,
+                    0,
+                ]  # green for non-colliding points
+                query_points_o3d.colors = o3d.utility.Vector3dVector(query_points_color)
+                o3d.visualization.draw_geometries(
+                    [self.mesh, query_points_o3d, frame], mesh_show_back_face=True
+                )
+        return is_colliding, max_penetration
+
     def query(
         self,
         query_mesh_verts: torch.Tensor,
@@ -98,24 +151,25 @@ def query(
 
         if cfg.debug:
             # visualize result
-            query_points_o3d = o3d.geometry.PointCloud()
-            query_points_o3d.points = o3d.utility.Vector3dVector(query_points_np)
-            query_points_o3d.transform(poses[-1].to("cpu").numpy())
-            query_points_color = np.zeros_like(query_points_np)
-            query_points_color[collision_result[-1].cpu().numpy()] = [
-                1.0,
-                0,
-                0,
-            ]  # red for colliding points
-            query_points_color[~collision_result[-1].cpu().numpy()] = [
-                0,
-                1.0,
-                0,
-            ]  # green for non-colliding points
-            query_points_o3d.colors = o3d.utility.Vector3dVector(query_points_color)
-            o3d.visualization.draw_geometries(
-                [self.mesh, query_points_o3d], mesh_show_back_face=True
-            )
+            for i in range(n_batch):
+                query_points_o3d = o3d.geometry.PointCloud()
+                query_points_o3d.points = o3d.utility.Vector3dVector(query_points_np)
+                query_points_o3d.transform(poses[i].to("cpu").numpy())
+                query_points_color = np.zeros_like(query_points_np)
+                query_points_color[collision_result[i].cpu().numpy()] = [
+                    1.0,
+                    0,
+                    0,
+                ]  # red for colliding points
+                query_points_color[~collision_result[i].cpu().numpy()] = [
+                    0,
+                    1.0,
+                    0,
+                ]  # green for non-colliding points
+                query_points_o3d.colors = o3d.utility.Vector3dVector(query_points_color)
+                o3d.visualization.draw_geometries(
+                    [self.mesh, query_points_o3d], mesh_show_back_face=True
+                )
         return is_colliding, max_penetration
 
     @staticmethod
@@ -310,23 +364,21 @@ def transform_points_batch(
 if __name__ == "__main__":
     from embodichain.data import get_data_path
 
-    bottle_a_path = get_data_path("ScannedBottle/moliwulong_processed.ply")
-    bottle_b_path = get_data_path("ScannedBottle/yibao_processed.ply")
-
-    bottle_a_mesh = trimesh.load(bottle_a_path)
-    bottle_b_mesh = trimesh.load(bottle_b_path)
-    bottle_a_verts = torch.tensor(bottle_a_mesh.vertices, dtype=torch.float32)
-    bottle_a_faces = torch.tensor(bottle_a_mesh.faces, dtype=torch.int64)
-    bottle_b_verts = torch.tensor(bottle_b_mesh.vertices, dtype=torch.float32)
-    bottle_b_faces = torch.tensor(bottle_b_mesh.faces, dtype=torch.int64)
+    mug_path = get_data_path("CoffeeCup/cup.ply")
+    mug_path = get_data_path("ScannedBottle/moliwulong_processed.ply")
+    mug_mesh = trimesh.load(mug_path, force="mesh", process=False)
+    verts = torch.tensor(mug_mesh.vertices, dtype=torch.float32)
+    faces = torch.tensor(mug_mesh.faces, dtype=torch.int32)
+    collision_checker = BatchConvexCollisionChecker(
+        verts, faces, max_decomposition_hulls=16
+    )
 
-    collision_checker = BatchConvexCollisionChecker(bottle_a_verts, bottle_a_faces)
     poses = torch.tensor(
         [
             [
                 [1, 0, 0, 0],
                 [0, 1, 0, 0],
-                [0, 0, 1, 1.0],
+                [0, 0, 1, 0.05],
                 [0, 0, 0, 1],
             ],
             [
@@ -337,13 +389,26 @@ def transform_points_batch(
             ],
         ]
     )
-    check_cfg = BatchConvexCollisionCheckerCfg(
-        debug=False,
-        n_query_mesh_samples=32768,
-        collsion_threshold=-0.003,
+    from scipy.spatial.transform import Rotation
+
+    rot = Rotation.from_euler("xyz", [12, 3, 32], degrees=True).as_matrix()
+    poses[0, :3, :3] = torch.tensor(rot, dtype=torch.float32)
+    poses[1, :3, :3] = torch.tensor(rot, dtype=torch.float32)
+
+    obj_path = get_data_path("ScannedBottle/yibao_processed.ply")
+    obj_mesh = trimesh.load(obj_path, force="mesh", process=False)
+    obj_verts = torch.tensor(obj_mesh.vertices, dtype=torch.float32)
+    obj_faces = torch.tensor(obj_mesh.faces, dtype=torch.int32)
+    test_pc = transform_points_batch(obj_verts, poses)
+
+    collision_checker.query_batch_points(
+        test_pc, collision_threshold=0.003, is_visual=True
     )
-    collisions, penetrations = collision_checker.query(
-        bottle_b_verts, bottle_b_faces, poses, cfg=check_cfg
+    collision_checker.query(
+        obj_verts,
+        obj_faces,
+        poses,
+        cfg=BatchConvexCollisionCheckerCfg(
+            debug=True, n_query_mesh_samples=32768, collsion_threshold=0.000
+        ),
     )
-    print("Collisions:", collisions)
-    print("Penetrations:", penetrations)
diff --git a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
index e69de29b..13dbe162 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
@@ -0,0 +1,230 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Sequence
+from .batch_collision_checker import BatchConvexCollisionChecker
+import torch
+
+
+@dataclass
+class SimpleGripperCollisionCfg:
+    max_open_length: float = 0.1
+    finger_length: float = 0.16
+    y_thickness: float = 0.03
+    x_thickness: float = 0.01
+    root_z_width: float = 0.06
+    device = torch.device("cpu")
+    rough_dense: float = 0.01
+    max_decomposition_hulls: int = 16
+
+
+class SimpleGripperCollisionChecker:
+    def __init__(
+        self,
+        object_mesh_verts: torch.Tensor,
+        object_mesh_faces: torch.Tensor,
+        cfg: SimpleGripperCollisionCfg = SimpleGripperCollisionCfg(),
+    ):
+        self._checker = BatchConvexCollisionChecker(
+            base_mesh_verts=object_mesh_verts,
+            base_mesh_faces=object_mesh_faces,
+            max_decomposition_hulls=cfg.max_decomposition_hulls,
+        )
+        self.cfg = cfg
+        self._init_pc_template()
+
+    def _init_pc_template(self):
+        self.root_template = box_surface_grid(
+            size=(
+                self.cfg.max_open_length,
+                self.cfg.y_thickness,
+                self.cfg.root_z_width,
+            ),
+            dense=self.cfg.rough_dense,
+        )
+        self.left_template = box_surface_grid(
+            size=(self.cfg.x_thickness, self.cfg.y_thickness, self.cfg.finger_length),
+            dense=self.cfg.rough_dense,
+        )
+        self.right_template = box_surface_grid(
+            size=(self.cfg.x_thickness, self.cfg.y_thickness, self.cfg.finger_length),
+            dense=self.cfg.rough_dense,
+        )
+
+    def _get_gripper_pc(
+        self, grasp_poses: torch.Tensor, open_lengths: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            grasp_poses: [B, 4, 4] homogeneous transformation matrix of the gripper root frame.
+            open_lengths: [B] opening length of the gripper fingers.
+        Returns:
+            gripper_pc: [B, P, 3] point cloud of the gripper in the world frame.
+        """
+
+        root_grasp_poses = grasp_poses.clone()
+        root_grasp_poses[:, :3, 3] -= (
+            root_grasp_poses[:, :3, 2]
+            * 0.5
+            * (self.cfg.finger_length + self.cfg.root_z_width)
+        )
+        open_lengths_repeat = open_lengths[:, None].repeat(1, 3)
+        left_finger_poses = grasp_poses.clone()
+        left_finger_poses[:, :3, 3] -= left_finger_poses[:, :3, 0] * open_lengths_repeat
+
+        right_finger_poses = grasp_poses.clone()
+        right_finger_poses[:, :3, 3] += (
+            right_finger_poses[:, :3, 0] * open_lengths_repeat
+        )
+
+        root_pc = transform_points_batch(self.root_template, root_grasp_poses)
+        left_pc = transform_points_batch(self.left_template, left_finger_poses)
+        right_pc = transform_points_batch(self.right_template, right_finger_poses)
+        gripper_pc = torch.cat([root_pc, left_pc, right_pc], dim=1)
+        return gripper_pc
+
+    def query(
+        self,
+        obj_pose: torch.Tensor,
+        grasp_poses: torch.Tensor,
+        open_lengths: torch.Tensor,
+    ) -> torch.Tensor:
+        inv_obj_pose = obj_pose.clone()
+        inv_obj_pose[:3, :3] = obj_pose[:3, :3].T
+        inv_obj_pose[:3, 3] = -obj_pose[:3, 3] @ obj_pose[:3, :3]
+        inv_obj_poses = inv_obj_pose[None, :, :].repeat(grasp_poses.shape[0], 1, 1)
+        grasp_relative_pose = torch.bmm(inv_obj_poses, grasp_poses)
+        gripper_pc = self._get_gripper_pc(grasp_relative_pose, open_lengths)
+        return self._checker.query_batch_points(
+            gripper_pc, collision_threshold=0.005, is_visual=False
+        )
+
+
+def transform_points_batch(
+    points: torch.Tensor, poses: torch.Tensor  # [P, 3]  # [B, 4, 4]
+) -> torch.Tensor:
+    """
+    Apply a batch of rigid transforms to a point cloud.
+
+    Args:
+        points: [P, 3] source point cloud.
+        poses: [B, 4, 4] batch of homogeneous transformation matrices.
+
+    Returns:
+        transformed: [B, P, 3] transformed point cloud for each pose.
+    """
+    R = poses[:, :3, :3]  # [B, 3, 3]
+    t = poses[:, :3, 3]  # [B, 3]
+    transformed = torch.einsum("bij, pj -> bpi", R, points) + t.unsqueeze(1)
+    return transformed
+
+
+def box_surface_grid(
+    size: Sequence[float] | torch.Tensor,
+    dense: float,
+    device: torch.device | str = "cpu",
+) -> torch.Tensor:
+    """Generate grid-sampled points on the surface of an axis-aligned box.
+
+    Six faces of the box are each sampled independently on a regular 2-D grid.
+    Grid resolution per face is derived automatically from ``dense``:
+    the number of sample points along an edge of length *L* is
+    ``max(2, round(L * dense) + 1)``, so ``dense`` behaves as
+    *approximate samples per unit length*.
+
+    Edge and corner points are shared across adjacent faces and are included
+    exactly once (no duplicates).
+
+    Args:
+        size: Box dimensions ``(sx, sy, sz)``.  Accepts a sequence of three
+            floats or a 1-D :class:`torch.Tensor` of length 3.
+        dense: Approximate number of grid sample points per unit length along
+            each edge.  Higher values yield denser point clouds.
+        device: Target PyTorch device for the returned tensor.
+
+    Returns:
+        Float tensor of shape ``(N, 3)`` containing surface points expressed
+        in the box's local frame (origin at the box centre).
+
+    Example:
+        >>> pts = box_surface_grid((0.1, 0.06, 0.03), dense=200.0)
+        >>> pts.shape
+        torch.Size([..., 3])
+    """
+    if isinstance(size, torch.Tensor):
+        sx, sy, sz = size[0].item(), size[1].item(), size[2].item()
+    else:
+        sx, sy, sz = float(size[0]), float(size[1]), float(size[2])
+
+    hx, hy, hz = sx / 2.0, sy / 2.0, sz / 2.0
+
+    # ── grid resolution per axis (at least 2 points to span the full edge) ──
+    nx = max(2, round(sx / dense) + 1)
+    ny = max(2, round(sy / dense) + 1)
+    nz = max(2, round(sz / dense) + 1)
+
+    xs = torch.linspace(-hx, hx, nx, device=device)
+    ys = torch.linspace(-hy, hy, ny, device=device)
+    zs = torch.linspace(-hz, hz, nz, device=device)
+
+    # Interior slices (exclude first and last to avoid duplicate edges)
+    xs_inner = xs[1:-1]  # length nx-2
+    ys_inner = ys[1:-1]  # length ny-2
+
+    def _grid(
+        u: torch.Tensor, v: torch.Tensor, axis: int, offset: float
+    ) -> torch.Tensor:
+        """Build a flat (M, 3) tensor for one face grid.
+
+        Args:
+            u: 1-D tensor of coordinates along the first in-plane axis.
+            v: 1-D tensor of coordinates along the second in-plane axis.
+            axis: Normal axis of the face — 0 (±X), 1 (±Y), or 2 (±Z).
+            offset: Signed half-extent along ``axis``.
+
+        Returns:
+            Tensor of shape ``(len(u) * len(v), 3)``.
+        """
+        uu, vv = torch.meshgrid(u, v, indexing="ij")
+        uu = uu.reshape(-1)
+        vv = vv.reshape(-1)
+        cc = torch.full_like(uu, offset)
+        if axis == 0:
+            return torch.stack([cc, uu, vv], dim=-1)
+        elif axis == 1:
+            return torch.stack([uu, cc, vv], dim=-1)
+        else:
+            return torch.stack([uu, vv, cc], dim=-1)
+
+    # ─────────────────────────────────────────────────────────────────────────
+    # Build 6 faces.  To avoid duplicate points on shared edges/corners:
+    #   ±X faces  → full  NY × NZ  grids
+    #   ±Y faces  → (NX-2) × NZ   grids  (x-edges owned by ±X faces)
+    #   ±Z faces  → (NX-2) × (NY-2) grids  (x- and y-edges owned above)
+    # ─────────────────────────────────────────────────────────────────────────
+    faces: list[torch.Tensor] = [
+        _grid(ys, zs, axis=0, offset=-hx),  # −X face  (NY × NZ)
+        _grid(ys, zs, axis=0, offset=+hx),  # +X face  (NY × NZ)
+        _grid(xs_inner, zs, axis=1, offset=-hy),  # −Y face  ((NX-2) × NZ)
+        _grid(xs_inner, zs, axis=1, offset=+hy),  # +Y face  ((NX-2) × NZ)
+        _grid(xs_inner, ys_inner, axis=2, offset=-hz),  # −Z face
+        _grid(xs_inner, ys_inner, axis=2, offset=+hz),  # +Z face
+    ]
+
+    return torch.cat(faces, dim=0)
diff --git a/examples/sim/demo/grasp_mug.py b/examples/sim/demo/grasp_mug.py
index 6ff56d69..ac68c073 100644
--- a/examples/sim/demo/grasp_mug.py
+++ b/examples/sim/demo/grasp_mug.py
@@ -236,7 +236,7 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
         antipodal_sampler_cfg=AntipodalSamplerCfg(
             n_sample=5000, max_length=0.088, min_length=0.003
         ),
-        force_regenerate=True,  # force user to annotate grasp region each time
+        force_regenerate=False,  # force user to annotate grasp region each time
     )
     sim.open_window()
 

From 7c55249e4998a0bc410d68d79f40ee4cd0d80223 Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 30 Mar 2026 19:17:15 +0800
Subject: [PATCH 08/10] add collision checker

---
 embodichain/lab/sim/objects/rigid_object.py   |   8 +-
 .../graspkit/pg_grasp/antipodal_annotator.py  |  17 +-
 .../graspkit/pg_grasp/antipodal_sampler.py    |   2 +-
 .../pg_grasp/batch_collision_checker.py       | 168 +++++++++++-------
 .../pg_grasp/gripper_collision_checker.py     |  15 +-
 embodichain/utils/warp/__init__.py            |   2 +
 .../utils/warp/collision_checker/__init__.py  |  17 ++
 .../warp/collision_checker/convex_query.py    |  55 ++++++
 .../tutorials/grasp}/grasp_mug.py             |  16 +-
 9 files changed, 216 insertions(+), 84 deletions(-)
 create mode 100644 embodichain/utils/warp/collision_checker/__init__.py
 create mode 100644 embodichain/utils/warp/collision_checker/convex_query.py
 rename {examples/sim/demo => scripts/tutorials/grasp}/grasp_mug.py (95%)

diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index 4da6e632..f90eee63 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -1164,11 +1164,11 @@ def get_grasp_pose(
 
         poses = self.get_local_pose(to_matrix=True)
         poses = torch.as_tensor(poses, dtype=torch.float32, device=self.device)
-        grasp_poses = []
-        open_lengths = []
+        grasp_poses: tuple[torch.Tensor] = []
+        open_lengths: tuple[torch.Tensor] = []
         for pose in poses:
             grasp_pose, open_length = self._grasp_annotator.get_approach_grasp_poses(
-                self._hit_point_pairs, pose, approach_direction
+                self._hit_point_pairs, pose, approach_direction, is_visual=False
             )
             grasp_poses.append(grasp_pose)
             open_lengths.append(open_length)
@@ -1184,6 +1184,6 @@ def get_grasp_pose(
             self._grasp_annotator.visualize_grasp_pose(
                 obj_pose=poses[0],
                 grasp_pose=grasp_poses[0],
-                open_length=open_lengths[0],
+                open_length=open_lengths[0].item(),
             )
         return grasp_poses
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
index bf84a9f3..2770cbfe 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
@@ -364,6 +364,7 @@ def get_approach_grasp_poses(
         hit_point_pairs: torch.Tensor,
         object_pose: torch.Tensor,
         approach_direction: torch.Tensor,
+        is_visual: bool = False,
     ) -> torch.Tensor:
         """Get grasp pose given approach direction
 
@@ -380,7 +381,9 @@ def get_approach_grasp_poses(
         origin_points_ = self._apply_transform(origin_points, object_pose)
         hit_points_ = self._apply_transform(hit_points, object_pose)
         centers = (origin_points_ + hit_points_) / 2
-        center = centers.mean(dim=0)
+
+        mesh_vert_transformed = self._apply_transform(self.vertices, object_pose)
+        mesh_center = mesh_vert_transformed.mean(dim=0)
 
         # filter perpendicular antipodal point
         grasp_x = F.normalize(hit_points_ - origin_points_, dim=-1)
@@ -400,11 +403,13 @@ def get_approach_grasp_poses(
             origin_points_[valid_mask] - hit_points_[valid_mask], dim=-1
         )
         # select non-collide grasp poses
-
         is_colliding, max_penetration = self._collision_checker.query(
-            object_pose, valid_grasp_poses, valid_open_lengths
+            object_pose,
+            valid_grasp_poses,
+            valid_open_lengths,
+            is_visual=is_visual,
+            collision_threshold=0.0,
         )
-
         # get best grasp pose
         valid_grasp_poses = valid_grasp_poses[~is_colliding]
         valid_open_lengths = valid_open_lengths[~is_colliding]
@@ -416,10 +421,10 @@ def get_approach_grasp_poses(
         )
         positive_angle = torch.abs(torch.acos(cos_angle))
         angle_cost = torch.abs(positive_angle - 0.5 * torch.pi) / (0.5 * torch.pi)
-        center_distance = torch.norm(valid_centers - center, dim=-1)
+        center_distance = torch.norm(valid_centers - mesh_center, dim=-1)
         center_cost = center_distance / center_distance.max()
         length_cost = 1 - valid_open_lengths / valid_open_lengths.max()
-        total_cost = 0.4 * angle_cost + 0.3 * length_cost + 0.3 * center_cost
+        total_cost = 0.3 * angle_cost + 0.3 * length_cost + 0.4 * center_cost
         best_idx = torch.argmin(total_cost)
         best_grasp_pose = valid_grasp_poses[best_idx]
         best_open_length = valid_open_lengths[best_idx]
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
index 09e4858e..a840e147 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
@@ -25,7 +25,7 @@
 
 @dataclass
 class AntipodalSamplerCfg:
-    n_sample: int = 10000
+    n_sample: int = 20000
     """surface point sample number"""
     max_angle: float = np.pi / 12
     """maximum angle (in radians) to randomly disturb the ray direction for antipodal point sampling, used to increase the diversity of sampled antipodal points. Note that setting max_angle to 0 will disable the random disturbance and sample antipodal points strictly along the surface normals, which may result in less diverse antipodal points and may not be ideal for all objects or grasping scenarios."""
diff --git a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
index 25327bea..7cb35be9 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
@@ -1,3 +1,19 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
 import trimesh
 import numpy as np
 import torch
@@ -10,6 +26,9 @@
 import pickle
 import open3d as o3d
 from embodichain.utils import logger
+from embodichain.utils.warp import convex_signed_distance_kernel
+import warp as wp
+from embodichain.utils.device_utils import standardize_device_string
 
 CONVEX_CACHE_DIR = os.path.join(
     os.path.expanduser("~"), ".cache", "embodichain_cache", "convex_decomposition"
@@ -51,14 +70,84 @@ def __init__(
         )
 
         if not os.path.isfile(self.cache_path):
+            # [n_convex, n_max_faces, 4]: plane equations, normals(3) and offsets(1), padded with zeros if a hull has less than n_max_faces
+            # [n_convex, ]: number of faces for each convex hull
+
             # generate convex hulls and extract plane equations, then cache to disk
-            self.plane_equations = BatchConvexCollisionChecker._compute_plane_equations(
+            plane_equations_np = BatchConvexCollisionChecker._compute_plane_equations(
                 base_mesh_verts_np, base_mesh_faces_np, max_decomposition_hulls
             )
+            # pack as a single tensor
+            n_convex = len(plane_equations_np)
+            n_max_equation = max(len(normals) for normals, _ in plane_equations_np)
+            plane_equations = torch.zeros(
+                size=(n_convex, n_max_equation, 4),
+                dtype=torch.float32,
+                device=self.device,
+            )
+            plane_equations_counts = torch.zeros(
+                n_convex, dtype=torch.int32, device=self.device
+            )
+            for i in range(n_convex):
+                n_equation = plane_equations_np[i][0].shape[0]
+                # plane normals
+                plane_equations[i, :n_equation, :3] = torch.tensor(
+                    plane_equations_np[i][0], device=self.device
+                )
+                # plane offsets
+                plane_equations[i, :n_equation, 3] = torch.tensor(
+                    plane_equations_np[i][1], device=self.device
+                )
+                plane_equations_counts[i] = n_equation
+            self.plane_equations = {
+                "plane_equations": plane_equations,
+                "plane_equation_counts": plane_equations_counts,
+            }
             pickle.dump(self.plane_equations, open(self.cache_path, "wb"))
         else:
-            # load precomputed plane equations from cache
             self.plane_equations = pickle.load(open(self.cache_path, "rb"))
+            self.plane_equations["plane_equations"] = self.plane_equations[
+                "plane_equations"
+            ].to(self.device)
+            self.plane_equations["plane_equation_counts"] = self.plane_equations[
+                "plane_equation_counts"
+            ].to(self.device)
+
+    @staticmethod
+    def batch_point_convex_query(
+        plane_equations: torch.Tensor,
+        plane_equation_counts: torch.Tensor,
+        batch_points: torch.Tensor,
+        device: torch.device,
+        collision_threshold: float = -0.003,
+    ):
+        plane_equations_wp = wp.from_torch(plane_equations)
+        plane_equation_counts_wp = wp.from_torch(plane_equation_counts)
+        batch_points_wp = wp.from_torch(batch_points)
+
+        n_pose = batch_points.shape[0]
+        n_point = batch_points.shape[1]
+        n_convex = plane_equations.shape[0]
+        point_convex_signed_distance_wp = wp.full(
+            shape=(n_pose, n_point, n_convex),
+            value=-float("inf"),
+            dtype=float,
+            device=standardize_device_string(device),
+        )  # [n_pose, n_point, n_convex]
+        wp.launch(
+            kernel=convex_signed_distance_kernel,
+            dim=(n_pose, n_point, n_convex),
+            inputs=(batch_points_wp, plane_equations_wp, plane_equation_counts_wp),
+            outputs=(point_convex_signed_distance_wp,),
+            device=standardize_device_string(device),
+        )
+        point_convex_signed_distance = wp.to_torch(point_convex_signed_distance_wp)
+        # import ipdb; ipdb.set_trace()
+        point_signed_distance = point_convex_signed_distance.min(
+            dim=-1
+        ).values  # [n_pose, n_point]
+        is_point_collide = point_signed_distance <= collision_threshold
+        return point_signed_distance, is_point_collide
 
     def query_batch_points(
         self,
@@ -67,27 +156,17 @@ def query_batch_points(
         is_visual: bool = False,
     ):
         n_batch = batch_points.shape[0]
-        n_points = batch_points.shape[1]
-        penetration_result = torch.zeros(size=(n_batch, n_points), device=self.device)
-        penetration_result.fill_(-float("inf"))
-        collision_result = torch.zeros(
-            size=(n_batch, n_points), dtype=torch.bool, device=self.device
-        )
-        collision_result.fill_(False)
-        for normals, offsets in self.plane_equations:
-            normals_torch = torch.tensor(normals, device=self.device)
-            offsets_torch = torch.tensor(offsets, device=self.device)
-            penetration, collides = check_collision_single_hull(
-                normals_torch,
-                offsets_torch,
+        point_signed_distance, is_point_collide = (
+            BatchConvexCollisionChecker.batch_point_convex_query(
+                self.plane_equations["plane_equations"],
+                self.plane_equations["plane_equation_counts"],
                 batch_points,
-                collision_threshold,
+                device=self.device,
+                collision_threshold=collision_threshold,
             )
-            penetration_result = torch.max(penetration_result, penetration)
-            collision_result = torch.logical_or(collision_result, collides)
-        is_colliding = collision_result.any(dim=-1)  # [B]
-        max_penetration = penetration_result.max(dim=-1)[0]  # [B]
-
+        )
+        is_pose_collide = is_point_collide.any(dim=-1)  # [B]
+        pose_surface_distance = point_signed_distance.min(dim=-1).values  # [B]
         if is_visual:
             # visualize result
             frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.1)
@@ -96,12 +175,12 @@ def query_batch_points(
                 query_points_np = batch_points[i].cpu().numpy()
                 query_points_o3d.points = o3d.utility.Vector3dVector(query_points_np)
                 query_points_color = np.zeros_like(query_points_np)
-                query_points_color[collision_result[i].cpu().numpy()] = [
+                query_points_color[is_point_collide[i].cpu().numpy()] = [
                     1.0,
                     0,
                     0,
                 ]  # red for colliding points
-                query_points_color[~collision_result[i].cpu().numpy()] = [
+                query_points_color[~is_point_collide[i].cpu().numpy()] = [
                     0,
                     1.0,
                     0,
@@ -110,7 +189,7 @@ def query_batch_points(
                 o3d.visualization.draw_geometries(
                     [self.mesh, query_points_o3d, frame], mesh_show_back_face=True
                 )
-        return is_colliding, max_penetration
+        return is_pose_collide, pose_surface_distance
 
     def query(
         self,
@@ -301,47 +380,6 @@ def check_collision_single_hull(
     return penetration, collides
 
 
-def merge_collision_results(
-    hull_results: List[Tuple[torch.Tensor, torch.Tensor]], device: torch.device
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Merge collision detection results from multiple convex hulls.
-
-    A pose is considered colliding if ANY point penetrates ANY convex hull.
-    The reported penetration depth is the maximum across all points and all hulls.
-
-    Args:
-        hull_results: List of (penetration [B, P], collides [B, P]) tuples,
-                      one per convex hull, as returned by check_collision_single_hull.
-        device: torch device.
-
-    Returns:
-        overall_collisions: [B] boolean, True if the pose collides with any hull.
-        overall_max_penetrations: [B] float, maximum penetration depth per pose.
-    """
-    if not hull_results:
-        raise ValueError("hull_results is empty, nothing to merge.")
-
-    B = hull_results[0][0].shape[0]
-
-    overall_collisions = torch.zeros(B, dtype=torch.bool, device=device)
-    overall_max_penetrations = torch.full(
-        (B,), -float("inf"), dtype=torch.float32, device=device
-    )
-
-    for penetration, collides in hull_results:
-        # Update collision flag: OR across hulls
-        # A pose collides if any point collides with this hull
-        overall_collisions |= collides.any(dim=-1)  # [B]
-
-        # Update max penetration: take the per-pose maximum across all points for this hull,
-        # then compare with the running maximum
-        hull_max_pen = penetration.max(dim=-1)[0]  # [B]
-        overall_max_penetrations = torch.max(overall_max_penetrations, hull_max_pen)
-
-    return overall_collisions, overall_max_penetrations
-
-
 def transform_points_batch(
     points: torch.Tensor, poses: torch.Tensor  # [P, 3]  # [B, 4, 4]
 ) -> torch.Tensor:
diff --git a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
index 13dbe162..42dfeb1a 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
@@ -30,8 +30,9 @@ class SimpleGripperCollisionCfg:
     x_thickness: float = 0.01
     root_z_width: float = 0.06
     device = torch.device("cpu")
-    rough_dense: float = 0.01
+    rough_dense: float = 0.015
     max_decomposition_hulls: int = 16
+    open_check_margin: float = 0.01
 
 
 class SimpleGripperCollisionChecker:
@@ -46,6 +47,7 @@ def __init__(
             base_mesh_faces=object_mesh_faces,
             max_decomposition_hulls=cfg.max_decomposition_hulls,
         )
+        self.device = object_mesh_verts.device
         self.cfg = cfg
         self._init_pc_template()
 
@@ -57,14 +59,17 @@ def _init_pc_template(self):
                 self.cfg.root_z_width,
             ),
             dense=self.cfg.rough_dense,
+            device=self.device,
         )
         self.left_template = box_surface_grid(
             size=(self.cfg.x_thickness, self.cfg.y_thickness, self.cfg.finger_length),
             dense=self.cfg.rough_dense,
+            device=self.device,
         )
         self.right_template = box_surface_grid(
             size=(self.cfg.x_thickness, self.cfg.y_thickness, self.cfg.finger_length),
             dense=self.cfg.rough_dense,
+            device=self.device,
         )
 
     def _get_gripper_pc(
@@ -84,7 +89,9 @@ def _get_gripper_pc(
             * 0.5
             * (self.cfg.finger_length + self.cfg.root_z_width)
         )
-        open_lengths_repeat = open_lengths[:, None].repeat(1, 3)
+        open_lengths_repeat = (
+            open_lengths[:, None] + self.cfg.open_check_margin
+        ).repeat(1, 3)
         left_finger_poses = grasp_poses.clone()
         left_finger_poses[:, :3, 3] -= left_finger_poses[:, :3, 0] * open_lengths_repeat
 
@@ -104,6 +111,8 @@ def query(
         obj_pose: torch.Tensor,
         grasp_poses: torch.Tensor,
         open_lengths: torch.Tensor,
+        collision_threshold: float = 0.0,
+        is_visual: bool = False,
     ) -> torch.Tensor:
         inv_obj_pose = obj_pose.clone()
         inv_obj_pose[:3, :3] = obj_pose[:3, :3].T
@@ -112,7 +121,7 @@ def query(
         grasp_relative_pose = torch.bmm(inv_obj_poses, grasp_poses)
         gripper_pc = self._get_gripper_pc(grasp_relative_pose, open_lengths)
         return self._checker.query_batch_points(
-            gripper_pc, collision_threshold=0.005, is_visual=False
+            gripper_pc, collision_threshold=collision_threshold, is_visual=is_visual
         )
 
 
diff --git a/embodichain/utils/warp/__init__.py b/embodichain/utils/warp/__init__.py
index 905bc9e7..e0fac57a 100644
--- a/embodichain/utils/warp/__init__.py
+++ b/embodichain/utils/warp/__init__.py
@@ -30,3 +30,5 @@
     repeat_first_point,
     interpolate_along_distance,
 )
+
+from .collision_checker.convex_query import convex_signed_distance_kernel
diff --git a/embodichain/utils/warp/collision_checker/__init__.py b/embodichain/utils/warp/collision_checker/__init__.py
new file mode 100644
index 00000000..d7e19801
--- /dev/null
+++ b/embodichain/utils/warp/collision_checker/__init__.py
@@ -0,0 +1,17 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+from . import convex_query
diff --git a/embodichain/utils/warp/collision_checker/convex_query.py b/embodichain/utils/warp/collision_checker/convex_query.py
new file mode 100644
index 00000000..f321e462
--- /dev/null
+++ b/embodichain/utils/warp/collision_checker/convex_query.py
@@ -0,0 +1,55 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ----------------------------------------------------------------------------
+
+import warp as wp
+from typing import Any
+
+
+@wp.kernel(enable_backward=False)
+def convex_signed_distance_kernel(
+    query_points: wp.array(dtype=wp.float32, ndim=3),
+    plane_equations: wp.array(dtype=wp.float32, ndim=3),
+    plane_equation_counts: wp.array(dtype=wp.int32, ndim=1),
+    signed_distances: wp.array(dtype=wp.float32, ndim=3),
+):
+    """
+    Compute the signed distance from query points to convex hulls defined by plane equations.
+
+    Args:
+        query_points: [n_pose, n_point, 3] coordinates of query points.
+        plane_equations: [n_convex, n_max_equation, 4] plane equations of convex hulls, where each plane equation is represented as (normal_x, normal_y, normal_z, offset).
+        plane_equation_counts: [n_convex, ] number of valid plane equations for each convex hull.
+
+    Returns:
+        signed_distances: [n_pose, n_point, n_convex] output signed distances from query points to convex hulls. Should be initialized as +inf before calling this kernel.
+    """
+    pose_id, point_id, convex_id = wp.tid()
+    n_equation = plane_equation_counts[convex_id]
+    for i in range(n_equation):
+        normal_x = plane_equations[convex_id, i, 0]
+        normal_y = plane_equations[convex_id, i, 1]
+        normal_z = plane_equations[convex_id, i, 2]
+        offset = plane_equations[convex_id, i, 3]
+        signed_distance = (
+            query_points[pose_id, point_id, 0] * normal_x
+            + query_points[pose_id, point_id, 1] * normal_y
+            + query_points[pose_id, point_id, 2] * normal_z
+            + offset
+        )
+        # should initialize as -inf
+        signed_distances[pose_id, point_id, convex_id] = max(
+            signed_distance, signed_distances[pose_id, point_id, convex_id]
+        )
diff --git a/examples/sim/demo/grasp_mug.py b/scripts/tutorials/grasp/grasp_mug.py
similarity index 95%
rename from examples/sim/demo/grasp_mug.py
rename to scripts/tutorials/grasp/grasp_mug.py
index ac68c073..5a7c89da 100644
--- a/examples/sim/demo/grasp_mug.py
+++ b/scripts/tutorials/grasp/grasp_mug.py
@@ -68,7 +68,7 @@ def parse_arguments():
     parser.add_argument(
         "--device",
         type=str,
-        default="cpu",
+        default="cuda",
         help="device to run the environment on, e.g., 'cpu' or 'cuda'",
     )
     return parser.parse_args()
@@ -182,7 +182,7 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     rest_arm_qpos = robot.get_qpos("arm")
 
     approach_xpos = grasp_xpos.clone()
-    approach_xpos[:, 2, 3] += 0.04
+    approach_xpos[:, 2, 3] += 0.1
 
     _, qpos_approach = robot.compute_ik(
         pose=approach_xpos, joint_seed=rest_arm_qpos, name="arm"
@@ -219,12 +219,14 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     )
     all_trajectory = torch.cat([arm_trajectory, hand_trajectory], dim=-1)
     interp_trajectory = interpolate_with_distance(
-        trajectory=all_trajectory, interp_num=300, device=sim.device
+        trajectory=all_trajectory, interp_num=200, device=sim.device
     )
     return interp_trajectory
 
 
 if __name__ == "__main__":
+    import time
+
     args = parse_arguments()
     sim = initialize_simulation(args)
     robot = create_robot(sim, position=[0.0, 0.0, 0.0])
@@ -234,15 +236,17 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     grasp_cfg = GraspAnnotatorCfg(
         viser_port=11801,
         antipodal_sampler_cfg=AntipodalSamplerCfg(
-            n_sample=5000, max_length=0.088, min_length=0.003
+            n_sample=20000, max_length=0.088, min_length=0.003
         ),
-        force_regenerate=False,  # force user to annotate grasp region each time
+        force_regenerate=True,  # force user to annotate grasp region each time
     )
     sim.open_window()
 
     # 1. View grasp object in browser (e.g http://localhost:11801)
     # 2. press 'Rect Select Region', select grasp region
     # 3. press 'Confirm Selection' to finish grasp region selection.
+
+    start_time = time.time()
     grasp_xpos = mug.get_grasp_pose(
         approach_direction=torch.tensor(
             [0, 0, -1], dtype=torch.float32, device=sim.device
@@ -250,6 +254,8 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
         cfg=grasp_cfg,
         is_visual=True,  # visualize selected grasp pose finally
     )
+    cost_time = time.time() - start_time
+    logger.log_info(f"Get grasp pose cost time: {cost_time:.2f} seconds")
 
     grab_traj = get_grasp_traj(sim, robot, grasp_xpos)
     input("Press Enter to start the grab mug demo...")

From 35dcb4407c6301340b629c8a3f71ccadc959e2ad Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 30 Mar 2026 19:33:41 +0800
Subject: [PATCH 09/10] update

---
 docs/source/tutorial/grasp_generator.rst | 77 ++++++++++++++++++++++++
 docs/source/tutorial/index.rst           |  1 +
 2 files changed, 78 insertions(+)
 create mode 100644 docs/source/tutorial/grasp_generator.rst

diff --git a/docs/source/tutorial/grasp_generator.rst b/docs/source/tutorial/grasp_generator.rst
new file mode 100644
index 00000000..51e802e8
--- /dev/null
+++ b/docs/source/tutorial/grasp_generator.rst
@@ -0,0 +1,77 @@
+Generating and Executing Robot Grasps
+======================================
+
+.. currentmodule:: embodichain.lab.sim
+
+This tutorial demonstrates how to generate antipodal grasp poses for a target object and execute a full grasp trajectory with a robot arm. It covers scene initialization, robot and object creation, interactive grasp region annotation, grasp pose computation, and trajectory execution in the simulation loop.
+
+The Code
+~~~~~~~~
+
+The tutorial corresponds to the ``grasp_generator.py`` script in the ``scripts/tutorials/grasp`` directory.
+
+.. dropdown:: Code for grasp_generator.py
+   :icon: code
+
+   .. literalinclude:: ../../../scripts/tutorials/grasp/grasp_generator.py
+      :language: python
+      :linenos:
+
+
+The Code Explained
+~~~~~~~~~~~~~~~~~~
+
+Configuring the simulation
+--------------------------
+
+Command-line arguments are parsed with ``argparse`` to select the number of parallel environments, the compute device, and optional rendering features such as ray tracing and headless mode.
+
+.. literalinclude:: ../../../scripts/tutorials/grasp/grasp_generator.py
+   :language: python
+   :start-at: def parse_arguments():
+   :end-at: return parser.parse_args()
+
+The parsed arguments are passed to ``initialize_simulation``, which builds a :class:`SimulationManagerCfg` and creates the :class:`SimulationManager` instance. When ray tracing is enabled a directional :class:`cfg.LightCfg` is also added to the scene.
+
+.. literalinclude:: ../../../scripts/tutorials/grasp/grasp_generator.py
+   :language: python
+   :start-at: def initialize_simulation(args) -> SimulationManager:
+   :end-at: return sim
+
+Annotating and computing grasp poses
+-------------------------------------
+
+Grasp generation is performed by :meth:`objects.RigidObject.get_grasp_pose`, which internally runs an antipodal sampler on the object mesh. A :class:`toolkits.graspkit.pg_grasp.GraspAnnotatorCfg` controls sampler parameters (sample count, gripper jaw limits) and the interactive annotation workflow:
+
+1. Open the visualization in a browser at the reported port (e.g. ``http://localhost:11801``).
+2. Use *Rect Select Region* to highlight the area of the object that should be grasped.
+3. Click *Confirm Selection* to finalize the region.
+
+The function returns a batch of ``(N_envs, 4, 4)`` homogeneous transformation matrices representing candidate grasp frames in the world coordinate system.
+
+For each grasp pose, gripper approach direction in world coordinate is required to compute the antipodal grasp. In this tutorial, we use a fixed approach direction (straight down in world frame) for simplicity, but it can be customized based on the task or object geometry.
+
+.. literalinclude:: ../../../scripts/tutorials/grasp/grasp_generator.py
+   :language: python
+   :start-at: # get mug grasp pose
+   :end-at: logger.log_info(f"Get grasp pose cost time: {cost_time:.2f} seconds")
+
+
+The Code Execution
+~~~~~~~~~~~~~~~~~~
+
+To run the script, execute the following command from the project root:
+
+.. code-block:: bash
+
+   python scripts/tutorials/grasp/grasp_generator.py
+
+A simulation window will open showing the robot and the mug. A browser-based visualizer will also launch (default port ``11801``) for interactive grasp region annotation.
+
+You can customize the run with additional arguments:
+
+.. code-block:: bash
+
+   python scripts/tutorials/grasp/grasp_generator.py --num_envs <n> --device <cuda/cpu> --enable_rt --headless
+
+After confirming the grasp region in the browser, the script will compute a grasp pose, print the elapsed time, and then wait for you to press **Enter** before executing the full grasp trajectory in the simulation. Press **Enter** again to exit once the motion is complete.
diff --git a/docs/source/tutorial/index.rst b/docs/source/tutorial/index.rst
index 05154047..ac58290c 100644
--- a/docs/source/tutorial/index.rst
+++ b/docs/source/tutorial/index.rst
@@ -14,6 +14,7 @@ Tutorials
    sensor
    motion_gen
    gizmo
+   grasp_generator
    basic_env
    modular_env
    rl

From 508a7121802a4c158437b208b0689e4acc9c7cfb Mon Sep 17 00:00:00 2001
From: chenjian <chenjian@dexforce.com>
Date: Mon, 30 Mar 2026 19:41:33 +0800
Subject: [PATCH 10/10] add comments

---
 embodichain/lab/sim/objects/rigid_object.py   |  2 +-
 .../graspkit/pg_grasp/antipodal_annotator.py  | 16 ++++++++++++--
 .../graspkit/pg_grasp/antipodal_sampler.py    | 10 +++++++--
 .../pg_grasp/batch_collision_checker.py       | 22 ++++++++++++++++++-
 .../pg_grasp/gripper_collision_checker.py     | 11 ++++++++++
 .../{grasp_mug.py => grasp_generator.py}      |  1 +
 6 files changed, 56 insertions(+), 6 deletions(-)
 rename scripts/tutorials/grasp/{grasp_mug.py => grasp_generator.py} (98%)

diff --git a/embodichain/lab/sim/objects/rigid_object.py b/embodichain/lab/sim/objects/rigid_object.py
index f90eee63..a9cde20a 100644
--- a/embodichain/lab/sim/objects/rigid_object.py
+++ b/embodichain/lab/sim/objects/rigid_object.py
@@ -1167,7 +1167,7 @@ def get_grasp_pose(
         grasp_poses: tuple[torch.Tensor] = []
         open_lengths: tuple[torch.Tensor] = []
         for pose in poses:
-            grasp_pose, open_length = self._grasp_annotator.get_approach_grasp_poses(
+            grasp_pose, open_length = self._grasp_annotator.get_grasp_poses(
                 self._hit_point_pairs, pose, approach_direction, is_visual=False
             )
             grasp_poses.append(grasp_pose)
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
index 2770cbfe..54cac47d 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_annotator.py
@@ -59,12 +59,20 @@ class SelectResult:
 
 
 class GraspAnnotator:
+    """GraspAnnotator provides functionality to annotate antipodal grasp regions on a given object mesh. It allows users to interactively select regions on the mesh and generates antipodal point pairs for grasping based on the selected region. The annotator also includes a collision checker to filter out infeasible grasp poses and can visualize the generated grasp poses in a 3D viewer.
+    """
     def __init__(
         self,
         vertices: torch.Tensor,
         triangles: torch.Tensor,
         cfg: GraspAnnotatorCfg = GraspAnnotatorCfg(),
     ) -> None:
+        """Initialize the GraspAnnotator with the given mesh vertices, triangles, and configuration.
+        Args:
+            vertices (torch.Tensor): A tensor of shape (V, 3) representing the vertex positions of the mesh.
+            triangles (torch.Tensor): A tensor of shape (F, 3) representing the triangle indices of the mesh.
+            cfg (GraspAnnotatorCfg, optional): Configuration for the grasp annotator. Defaults to GraspAnnotatorCfg().
+        """
         self.device = vertices.device
         self.vertices = vertices
         self.triangles = triangles
@@ -82,7 +90,11 @@ def __init__(
         self.cfg = cfg
         self.antipodal_sampler = AntipodalSampler(cfg=cfg.antipodal_sampler_cfg)
 
-    def annotate(self):
+    def annotate(self) -> torch.Tensor:
+        """Annotate antipodal grasp region on the mesh and return sampled antipodal point pairs.
+        Returns:
+            torch.Tensor: A tensor of shape (N, 2, 3) representing N antipodal point pairs. Each pair consists of a hit point and its corresponding surface point.
+        """
         cache_path = self._get_cache_dir(self.vertices, self.triangles)
         if os.path.exists(cache_path) and not self.cfg.force_regenerate:
             logger.log_info(
@@ -359,7 +371,7 @@ def _apply_transform(points: torch.Tensor, transform: torch.Tensor) -> torch.Ten
         t = transform[:3, 3]
         return points @ r.T + t
 
-    def get_approach_grasp_poses(
+    def get_grasp_poses(
         self,
         hit_point_pairs: torch.Tensor,
         object_pose: torch.Tensor,
diff --git a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
index a840e147..cebcafde 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/antipodal_sampler.py
@@ -25,6 +25,7 @@
 
 @dataclass
 class AntipodalSamplerCfg:
+    """ Configuration for AntipodalSampler."""
     n_sample: int = 20000
     """surface point sample number"""
     max_angle: float = np.pi / 12
@@ -36,6 +37,7 @@ class AntipodalSamplerCfg:
 
 
 class AntipodalSampler:
+    """ AntipodalSampler samples antipodal point pairs on a given mesh. It uses Open3D's raycasting functionality to find points on the mesh that are visible along the negative normal direction from uniformly sampled points on the mesh surface. The sampler can also apply a random disturbance to the ray direction to increase the diversity of sampled antipodal points. The resulting antipodal point pairs can be used for grasp generation and annotation tasks."""
     def __init__(
         self,
         cfg: AntipodalSamplerCfg = AntipodalSamplerCfg(),
@@ -46,6 +48,10 @@ def __init__(
     def sample(self, vertices: torch.Tensor, faces: torch.Tensor) -> torch.Tensor:
         """Get sample Antipodal point pair
 
+        Args:
+            vertices: [V, 3] vertex positions of the mesh
+            faces: [F, 3] triangle indices of the mesh
+
         Returns:
             hit_point_pairs: [N, 2, 3] tensor of N antipodal point pairs. Each pair consists of a hit point and its corresponding surface point.
         """
@@ -83,13 +89,13 @@ def sample(self, vertices: torch.Tensor, faces: torch.Tensor) -> torch.Tensor:
         ray_origin = torch.vstack([ray_origin, ray_origin])
         ray_direc = torch.vstack([ray_direc, disturb_direc])
         # casting
-        return self.get_raycast_result(
+        return self._get_raycast_result(
             ray_origin,
             ray_direc,
             surface_origin=torch.vstack([sample_points, sample_points]),
         )
 
-    def get_raycast_result(
+    def _get_raycast_result(
         self,
         ray_origin: torch.Tensor,
         ray_direc: torch.Tensor,
diff --git a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
index 7cb35be9..a488108b 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/batch_collision_checker.py
@@ -37,18 +37,31 @@
 
 @dataclass
 class BatchConvexCollisionCheckerCfg:
+    """ Configuration for BatchConvexCollisionChecker."""
+
     collsion_threshold: float = 0.0
+    """ Collision threshold in meters. A point is considered colliding if its signed distance to the hull interior is <= this threshold. This allows for a margin of error in collision checking, where a small positive threshold can be used to consider points near the surface as colliding, and a small negative threshold can be used to allow for slight penetration without considering it a collision."""
     n_query_mesh_samples: int = 4096
+    """ Number of points to sample from the query mesh surface for collision checking. A higher number of samples can provide a more accurate collision check at the cost of increased computation time. The optimal number may depend on the complexity of the mesh and the required precision of collision detection."""
     debug: bool = False
+    """ Whether to visualize the collision checking results for debugging purposes. If set to True, the code will generate visualizations of the query points colored by their collision status (e.g., red for colliding points and green for non-colliding points) along with the original mesh. This can help in understanding and verifying the collision checking process, especially during development and testing."""
 
 
 class BatchConvexCollisionChecker:
+    """ BatchConvexCollisionChecker performs efficient collision checking between a batch of query point clouds and a convex decomposition of a mesh. The convex decomposition is represented by plane equations of the convex hulls, which are precomputed and cached for efficiency. The collision checking is done by computing the signed distance from each query point to the convex hulls using the plane equations, and determining if any points are colliding based on a specified collision threshold. This class can be used"""
+
     def __init__(
         self,
         base_mesh_verts: torch.Tensor,
         base_mesh_faces: torch.Tensor,
         max_decomposition_hulls: int = 32,
     ):
+        """ Initialize the BatchConvexCollisionChecker by performing convex decomposition on the input mesh and extracting plane equations for the convex hulls. The plane equations are cached to disk to avoid redundant computation in future runs.
+        Args:
+            base_mesh_verts: [N, 3] vertex positions of the input mesh.
+            base_mesh_faces: [M, 3] triangle indices of the input mesh.
+            max_decomposition_hulls: maximum number of convex hulls to decompose into. A higher number allows for a more accurate approximation of the original mesh but increases computation time and memory usage. The optimal number may depend on the complexity of the mesh and the required precision of collision checking.
+        """
         if not os.path.isdir(CONVEX_CACHE_DIR):
             os.makedirs(CONVEX_CACHE_DIR, exist_ok=True)
         self.device = base_mesh_verts.device
@@ -154,7 +167,14 @@ def query_batch_points(
         batch_points: torch.Tensor,
         collision_threshold: float = 0.0,
         is_visual: bool = False,
-    ):
+    ) -> torch.Tensor:
+        """ Query collision status for a batch of point clouds. The collision status is determined by checking if the signed distance from any point in the cloud to the convex hulls is less than or equal to the specified collision threshold.
+        Args:
+            batch_points: [B, n_point, 3] batch of point clouds to query for collision status.
+            collision_threshold: Collision threshold in meters. A point is considered colliding if its signed distance to the hull interior is <= this threshold. This allows for a margin of error in collision checking, where a small positive threshold can be used to consider points near the surface as colliding, and a small negative threshold can be used to allow for slight penetration without considering it a collision.
+            is_visual: Whether to visualize the collision checking results for debugging purposes. If set to True, the code will generate visualizations of the query points colored by their collision status (e.g., red for colliding points and green for non-colliding points) along with the original mesh. This can help in understanding and verifying the collision checking process, especially during development and testing.
+        Returns:
+            is_pose_collide: [B, ] boolean tensor indicating whether each point cloud in the"""
         n_batch = batch_points.shape[0]
         point_signed_distance, is_point_collide = (
             BatchConvexCollisionChecker.batch_point_convex_query(
diff --git a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
index 42dfeb1a..bacd6037 100644
--- a/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
+++ b/embodichain/toolkits/graspkit/pg_grasp/gripper_collision_checker.py
@@ -24,15 +24,26 @@
 
 @dataclass
 class SimpleGripperCollisionCfg:
+    """ Configuration for the SimpleGripperCollisionChecker. This class defines various parameters related to the gripper geometry, point cloud generation, and collision checking process. Users can customize these parameters based on the specific gripper being modeled and the requirements of the application."""
+
     max_open_length: float = 0.1
+    """ Maximum opening length of the gripper fingers. This should be set according to the specific gripper being modeled, and it defines the maximum distance between the two fingers when fully open."""
     finger_length: float = 0.16
+    """ Length of the gripper fingers from the root to the tip. This should be set according to the specific gripper being modeled, and it defines how far the fingers extend from the gripper root frame."""
     y_thickness: float = 0.03
+    """ Thickness of the gripper along the Y-axis (the axis perpendicular to the finger opening direction). This should be set according to the specific gripper being modeled, and it defines the width of the gripper's main body and fingers in the Y direction."""
     x_thickness: float = 0.01
+    """ Thickness of the gripper along the X-axis (the axis parallel to the finger opening direction). This should be set according to the specific gripper being modeled, and it defines the thickness of the fingers and the root in the X direction."""
     root_z_width: float = 0.06
+    """ Width of the gripper root along the Z-axis (the axis along the finger length direction). This should be set according to the specific gripper being modeled, and it defines how far the root extends along the Z direction."""
     device = torch.device("cpu")
+    """ Device on which the gripper point cloud will be generated and processed. This should be set according to the computational resources available and the requirements of the application. For example, if using a GPU for collision checking, this should be set to torch.device('cuda'). """
     rough_dense: float = 0.015
+    """ Approximate number of points per unit length for the gripper point cloud. Higher values will yield denser point clouds, which can improve collision checking accuracy but also increase computational cost. This should be set based on the desired balance between accuracy and efficiency for the specific application."""
     max_decomposition_hulls: int = 16
+    """ Maximum number of convex hulls to decompose the object mesh into for collision checking. This should be set based on the complexity of the object geometry and the desired accuracy of collision checking. More hulls can provide a tighter approximation of the object shape but will increase computational cost."""
     open_check_margin: float = 0.01
+    """ Additional margin added to the gripper open length when checking for collisions. This can help account for uncertainties in the gripper pose or object geometry, and can be set based on the specific requirements of the application."""
 
 
 class SimpleGripperCollisionChecker:
diff --git a/scripts/tutorials/grasp/grasp_mug.py b/scripts/tutorials/grasp/grasp_generator.py
similarity index 98%
rename from scripts/tutorials/grasp/grasp_mug.py
rename to scripts/tutorials/grasp/grasp_generator.py
index 5a7c89da..9f4450d0 100644
--- a/scripts/tutorials/grasp/grasp_mug.py
+++ b/scripts/tutorials/grasp/grasp_generator.py
@@ -242,6 +242,7 @@ def get_grasp_traj(sim: SimulationManager, robot: Robot, grasp_xpos: torch.Tenso
     )
     sim.open_window()
 
+    # Annotate part of the mug to be grasped by following the instructions in the visualization window:
     # 1. View grasp object in browser (e.g http://localhost:11801)
     # 2. press 'Rect Select Region', select grasp region
     # 3. press 'Confirm Selection' to finish grasp region selection.