import os, sys, math import json, time import numpy as np import argparse from isaacgym import gymtorch, gymapi import torch CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) CSV_FRAME_RATE = 50.0 CSV_TOTAL_COLS = 36 CSV_ROOT_POS_SLICE = slice(0, 3) CSV_ROOT_QUAT_SLICE = slice(3, 7) CSV_DOF_SLICE = slice(7, 36) def get_label_indices(labels, prefix): return [i for i, label in enumerate(labels) if label.startswith(prefix)] def load_motion_data_json(json_path): with open(json_path, 'r') as f: motion = json.load(f) labels = motion.get('Labels', []) frames = np.array(motion['Frames'], dtype=np.float32) # 提取 DOF、root_pos、root_quat 的列索引 dof_indices = get_label_indices(labels, "dof_pos/") root_pos_indices = get_label_indices(labels, "root_pos/") root_quat_indices = get_label_indices(labels, "root_quat/") print(f"Found {len(dof_indices)} DOF channels:") for idx in dof_indices: print(f" {labels[idx]}") print(f"root_pos indices: {[labels[i] for i in root_pos_indices]}") print(f"root_quat indices: {[labels[i] for i in root_quat_indices]}") return ( frames[:, dof_indices], frames[:, root_pos_indices], frames[:, root_quat_indices], motion['FrameDuration'] ) def load_motion_data_csv(csv_path): frames = np.loadtxt(csv_path, delimiter=',', dtype=np.float32) if frames.ndim == 1: frames = frames[None, :] if frames.shape[1] < CSV_TOTAL_COLS: raise ValueError( f"CSV requires at least {CSV_TOTAL_COLS} columns, got {frames.shape[1]}: {csv_path}" ) if frames.shape[1] > CSV_TOTAL_COLS: print( f"CSV has {frames.shape[1]} columns, using first {CSV_TOTAL_COLS} columns: {csv_path}" ) frames = frames[:, :CSV_TOTAL_COLS] print(f"Loaded CSV motion: {csv_path}") print(f"root_pos columns: {CSV_ROOT_POS_SLICE.start + 1}~{CSV_ROOT_POS_SLICE.stop}") print(f"root_quat columns: {CSV_ROOT_QUAT_SLICE.start + 1}~{CSV_ROOT_QUAT_SLICE.stop}") print(f"dof columns: {CSV_DOF_SLICE.start + 1}~{CSV_DOF_SLICE.stop}") return ( frames[:, CSV_DOF_SLICE], frames[:, CSV_ROOT_POS_SLICE], frames[:, CSV_ROOT_QUAT_SLICE], 1.0 / CSV_FRAME_RATE ) def load_motion_data(motion_path): ext = os.path.splitext(motion_path)[1].lower() if ext == ".json": return load_motion_data_json(motion_path) if ext == ".csv": return load_motion_data_csv(motion_path) raise ValueError(f"Unsupported motion file format: {motion_path}") def set_base_pose(gym, sim, env, actor, root_pos, root_quat): """ 设置机器人 base link 的位置和旋转 :param root_pos: [x, y, z] :param root_quat: [x, y, z, w] """ root_state_tensor = gym.acquire_actor_root_state_tensor(sim) root_state = gymtorch.wrap_tensor(root_state_tensor) # 获取 actor 在 tensor 中的索引（通常为 0，如果你只加载了一个 actor） actor_index = 0 # 更新 base 位置和四元数 root_state[actor_index, :3] = torch.tensor(root_pos, dtype=torch.float32) root_state[actor_index, 3:7] = torch.tensor(root_quat, dtype=torch.float32) # 写回物理引擎 gym.set_actor_root_state_tensor(sim, root_state_tensor) def set_dof_positions(gym, env, actor, target_pos): dof_states = np.zeros(len(target_pos), dtype=gymapi.DofState.dtype) dof_states['pos'] = target_pos dof_states['vel'] = np.zeros_like(target_pos) gym.set_actor_dof_states(env, actor, dof_states, gymapi.STATE_POS + gymapi.STATE_VEL) def parse_args(): parser = argparse.ArgumentParser(description="Isaac Gym Preview 4 Motion Visualizer") parser.add_argument('--file_name', type=str, default="mixamo/low_jump.json", help='Motion file name (.json or .csv). Default: "mixamo/low_jump.json"') parser.add_argument('--robot_type', type=str, choices=['adam_lite', 'adam_sp'], default='adam_lite', help='Robot type to load. Default: "adam_lite"') return parser.parse_args() def resolve_existing_path(candidates): for rel_path in candidates: abs_path = os.path.join(CURRENT_DIR, rel_path) if os.path.exists(abs_path): return rel_path raise FileNotFoundError(f"No valid path found in candidates: {candidates}") def get_robot_paths(robot_type): if robot_type == "adam_lite": urdf_path = resolve_existing_path([ "robot_description/adam_lite/urdf/adam_lite.urdf", "robot_description/adam_lite/adam_lite.urdf", ]) motion_dir = "adam_lite" elif robot_type == "adam_sp": urdf_path = resolve_existing_path([ "robot_description/adam_sp/urdf/adam_sp.urdf", "robot_description/adam_sp/adam_sp.urdf", ]) motion_dir = "adam_sp" else: raise ValueError(f"Unknown robot_type: {robot_type}") return urdf_path, motion_dir def main(): args = parse_args() # 加载机器人资产 asset_path, motion_dir = get_robot_paths(args.robot_type) asset_root = os.path.dirname(asset_path) asset_file = os.path.basename(asset_path) # 加载动作数据 motion_path = os.path.join(CURRENT_DIR, motion_dir, args.file_name) frames_dof, frames_root_pos, frames_root_quat, frame_duration = load_motion_data(motion_path) num_frames = len(frames_dof) current_frame = 0 # 初始化 gym gym = gymapi.acquire_gym() # 设置仿真参数 sim_params = gymapi.SimParams() sim_params.dt = frame_duration # 模拟频率 sim_params.substeps = 2 sim_params.up_axis = gymapi.UP_AXIS_Z sim_params.gravity = gymapi.Vec3(0.0, 0.0, 0.0) # 关闭重力 sim_params.use_gpu_pipeline = False # 禁用 GPU pipeline（更简单） sim = gym.create_sim(0, 0, gymapi.SIM_PHYSX, sim_params) if sim is None: raise Exception("Failed to create sim") # 加载地面 plane_params = gymapi.PlaneParams() plane_params.normal = gymapi.Vec3(0, 0, 1) gym.add_ground(sim, plane_params) asset_options = gymapi.AssetOptions() asset_options.fix_base_link = False asset_options.disable_gravity = True asset_options.collapse_fixed_joints = True robot_asset = gym.load_asset(sim, asset_root, asset_file, asset_options) # 获取 DOF 数量 num_dof = gym.get_asset_dof_count(robot_asset) if frames_dof.shape[1] != num_dof: print( f"DOF count mismatch: motion has {frames_dof.shape[1]}, " f"asset requires {num_dof}. Auto-aligning." ) if frames_dof.shape[1] > num_dof: frames_dof = frames_dof[:, :num_dof] else: pad = np.zeros((frames_dof.shape[0], num_dof - frames_dof.shape[1]), dtype=np.float32) frames_dof = np.concatenate([frames_dof, pad], axis=1) # 创建环境 spacing = 1.0 lower = gymapi.Vec3(-spacing, -spacing, 0.0) upper = gymapi.Vec3(spacing, spacing, spacing) env = gym.create_env(sim, lower, upper, 8) # 创建机器人 actor pose = gymapi.Transform() pose.p = gymapi.Vec3(0.0, 0.0, 1.0) pose.r = gymapi.Quat(0.0, 0.0, 0.0, 1.0) actor = gym.create_actor(env, robot_asset, pose, "actor", 0, 0) cam_props = gymapi.CameraProperties() viewer = gym.create_viewer(sim, cam_props) if viewer is None: raise Exception("Failed to create viewer") # 设置相机参数 CAMERA_DISTANCE = -3.0 # 距离 base 的距离 CAMERA_HEIGHT = 1.0 # 高度 CAMERA_ANGLE = math.radians(90) # 视角角度（弧度） while not gym.query_viewer_has_closed(viewer): if current_frame < num_frames: target_dof = frames_dof[current_frame] target_root_pos = frames_root_pos[current_frame] target_root_quat = frames_root_quat[current_frame] # 设置 Base 位姿 set_base_pose(gym, sim, env, actor, target_root_pos, target_root_quat) # 设置关节角度 set_dof_positions(gym, env, actor, target_dof) current_frame += 1 else: print("Animation finished.") break # Step simulation and render gym.simulate(sim) gym.fetch_results(sim, True) gym.step_graphics(sim) # 获取机器人 base 位置 root_state_tensor = gym.acquire_actor_root_state_tensor(sim) root_state = gymtorch.wrap_tensor(root_state_tensor) root_pos = root_state[0, :3].cpu().numpy() # 使用球面坐标系计算相机位置 cam_x = root_pos[0] + CAMERA_DISTANCE * math.cos(CAMERA_ANGLE) cam_y = root_pos[1] + CAMERA_DISTANCE * math.sin(CAMERA_ANGLE) cam_z = root_pos[2] + CAMERA_HEIGHT cam_pos = gymapi.Vec3(cam_x, cam_y, cam_z) cam_target = gymapi.Vec3(root_pos[0], root_pos[1], root_pos[2]) # 更新相机视角 gym.viewer_camera_look_at(viewer, None, cam_pos, cam_target) gym.draw_viewer(viewer, sim, True) gym.sync_frame_time(sim) time.sleep(0.001) if __name__ == "__main__": main()