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..6ff56d69 --- /dev/null +++ b/examples/sim/demo/grasp_mug.py @@ -0,0 +1,261 @@ +# ---------------------------------------------------------------------------- +# 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, # 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 world frame + cfg=grasp_cfg, + is_visual=True, # visualize selected grasp pose finally + ) + + 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...") 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]