| """ |
| Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. |
| |
| NVIDIA CORPORATION and its licensors retain all intellectual property |
| and proprietary rights in and to this software, related documentation |
| and any modifications thereto. Any use, reproduction, disclosure or |
| distribution of this software and related documentation without an express |
| license agreement from NVIDIA CORPORATION is strictly prohibited. |
| |
| Spherical Joint |
| ------------ |
| - Demonstrates usage of spherical joints. |
| """ |
|
|
| import math |
| import numpy as np |
| from isaacgym import gymapi, gymutil |
|
|
|
|
| def clamp(x, min_value, max_value): |
| return max(min(x, max_value), min_value) |
|
|
| |
|
|
|
|
| class AssetDesc: |
| def __init__(self, file_name, flip_visual_attachments=False): |
| self.file_name = file_name |
| self.flip_visual_attachments = flip_visual_attachments |
|
|
|
|
| asset_descriptors = [ |
| AssetDesc("urdf/spherical_joint.urdf", False), |
| |
| ] |
|
|
| |
| args = gymutil.parse_arguments( |
| description="Spherical Joint: Show example of controlling a spherical joint robot.", |
| ) |
|
|
| |
| gym = gymapi.acquire_gym() |
|
|
| |
| sim_params = gymapi.SimParams() |
| sim_params.dt = dt = 1.0 / 60.0 |
| sim_params.gravity = gymapi.Vec3(0, 0, 0) |
| sim_params.up_axis = gymapi.UP_AXIS_Z |
|
|
| if args.physics_engine == gymapi.SIM_FLEX: |
| pass |
| elif args.physics_engine == gymapi.SIM_PHYSX: |
| sim_params.physx.solver_type = 1 |
| sim_params.physx.num_position_iterations = 6 |
| sim_params.physx.num_velocity_iterations = 0 |
| sim_params.physx.num_threads = args.num_threads |
| sim_params.physx.use_gpu = args.use_gpu |
|
|
| sim_params.use_gpu_pipeline = False |
| if args.use_gpu_pipeline: |
| print("WARNING: Forcing CPU pipeline.") |
|
|
| sim = gym.create_sim(args.compute_device_id, args.graphics_device_id, args.physics_engine, sim_params) |
| if sim is None: |
| print("*** Failed to create sim") |
| quit() |
|
|
|
|
| |
| viewer = gym.create_viewer(sim, gymapi.CameraProperties()) |
| if viewer is None: |
| print("*** Failed to create viewer") |
| quit() |
|
|
| |
| asset_root = "../../assets" |
| asset_file = asset_descriptors[0].file_name |
|
|
| asset_options = gymapi.AssetOptions() |
| asset_options.fix_base_link = True |
| asset_options.flip_visual_attachments = asset_descriptors[0].flip_visual_attachments |
| asset_options.use_mesh_materials = True |
|
|
| print("Loading asset '%s' from '%s'" % (asset_file, asset_root)) |
| asset = gym.load_asset(sim, asset_root, asset_file, asset_options) |
|
|
| |
| dof_names = gym.get_asset_dof_names(asset) |
|
|
| |
| dof_props = gym.get_asset_dof_properties(asset) |
|
|
| |
| num_dofs = gym.get_asset_dof_count(asset) |
| dof_states = np.zeros(num_dofs, dtype=gymapi.DofState.dtype) |
|
|
| |
| dof_types = [gym.get_asset_dof_type(asset, i) for i in range(num_dofs)] |
|
|
| |
| dof_positions = dof_states['pos'] |
|
|
| |
| stiffnesses = dof_props['stiffness'] |
| dampings = dof_props['damping'] |
| armatures = dof_props['armature'] |
| has_limits = dof_props['hasLimits'] |
| lower_limits = dof_props['lower'] |
| upper_limits = dof_props['upper'] |
|
|
| |
| defaults = np.zeros(num_dofs) |
| for i in range(num_dofs): |
| if has_limits[i]: |
| if dof_types[i] == gymapi.DOF_ROTATION: |
| lower_limits[i] = clamp(lower_limits[i], -math.pi, math.pi) |
| upper_limits[i] = clamp(upper_limits[i], -math.pi, math.pi) |
| |
| if lower_limits[i] > 0.0: |
| defaults[i] = lower_limits[i] |
| elif upper_limits[i] < 0.0: |
| defaults[i] = upper_limits[i] |
| else: |
| |
| if dof_types[i] == gymapi.DOF_ROTATION: |
| |
| lower_limits[i] = -math.pi |
| upper_limits[i] = math.pi |
| elif dof_types[i] == gymapi.DOF_TRANSLATION: |
| |
| lower_limits[i] = -1.0 |
| upper_limits[i] = 1.0 |
| else: |
| print("Unknown DOF type!") |
| exit() |
| |
| dof_positions[i] = defaults[i] |
|
|
| |
| for i in range(num_dofs): |
| print("DOF %d" % i) |
| print(" Name: '%s'" % dof_names[i]) |
| print(" Type: %s" % gym.get_dof_type_string(dof_types[i])) |
| print(" Stiffness: %r" % stiffnesses[i]) |
| print(" Damping: %r" % dampings[i]) |
| print(" Armature: %r" % armatures[i]) |
| print(" Limited? %r" % has_limits[i]) |
| if has_limits[i]: |
| print(" Lower %f" % lower_limits[i]) |
| print(" Upper %f" % upper_limits[i]) |
|
|
| |
| num_envs = 1 |
| num_per_row = 6 |
| spacing = 2.5 |
| env_lower = gymapi.Vec3(-spacing, 0.0, -spacing) |
| env_upper = gymapi.Vec3(spacing, spacing, spacing) |
|
|
| |
| cam_pos = gymapi.Vec3(1.0, 1.0, 1) |
| cam_target = gymapi.Vec3(0, 0, 0) |
| gym.viewer_camera_look_at(viewer, None, cam_pos, cam_target) |
|
|
| |
| envs = [] |
| actor_handles = [] |
|
|
| print("Creating %d environments" % num_envs) |
| for i in range(num_envs): |
| |
| env = gym.create_env(sim, env_lower, env_upper, num_per_row) |
| envs.append(env) |
|
|
| |
| pose = gymapi.Transform() |
| pose.p = gymapi.Vec3(0.0, 0.0, 0.0) |
| |
|
|
| actor_handle = gym.create_actor(env, asset, pose, "actor", i, 1) |
| actor_handles.append(actor_handle) |
|
|
| props = gym.get_actor_dof_properties(env, actor_handle) |
| props["driveMode"] = (gymapi.DOF_MODE_POS, gymapi.DOF_MODE_POS, gymapi.DOF_MODE_POS, gymapi.DOF_MODE_POS, gymapi.DOF_MODE_POS, gymapi.DOF_MODE_POS) |
| props["stiffness"] = (50.0, 50.0, 50.0, 50.0, 50.0, 50.0) |
| props["damping"] = (10.0, 10.0, 10.0, 100.0, 100.0, 100.0) |
|
|
| gym.set_actor_dof_properties(env, actor_handle, props) |
|
|
| |
| gym.set_actor_dof_states(env, actor_handle, dof_states, gymapi.STATE_ALL) |
|
|
|
|
| def random_quaternion(): |
| """Random quaternion of the form (x, y, z, w). |
| |
| Returns: |
| np.ndarray: 4-element array. |
| """ |
| r1, r2, r3 = np.random.random(3) |
|
|
| q1 = math.sqrt(1.0 - r1) * (math.sin(2 * math.pi * r2)) |
| q2 = math.sqrt(1.0 - r1) * (math.cos(2 * math.pi * r2)) |
| q3 = math.sqrt(r1) * (math.sin(2 * math.pi * r3)) |
| q4 = math.sqrt(r1) * (math.cos(2 * math.pi * r3)) |
|
|
| quat_xyzw = np.array([q2, q3, q4, q1]) |
|
|
| if quat_xyzw[-1] < 0: |
| quat_xyzw = -quat_xyzw |
|
|
| return quat_xyzw |
|
|
|
|
| def quat2expcoord(q): |
| """Converts quaternion to exponential coordinates. |
| |
| Args: |
| q (np.ndarray): Quaternion as a 4-element array of the form [x, y, z, w]. |
| |
| Returns: |
| np.ndarray: Exponential coordinate as 3-element array. |
| """ |
| if (q[-1] < 0): |
| q = -q |
|
|
| theta = 2. * math.atan2(np.linalg.norm(q[:-1]), q[-1]) |
| w = (1. / np.sin(theta/2.0)) * q[:-1] |
|
|
| return w * theta |
|
|
|
|
| |
| axes_geom = gymutil.AxesGeometry(0.5) |
|
|
| cnt = 0 |
| while not gym.query_viewer_has_closed(viewer): |
|
|
| |
| gym.simulate(sim) |
| gym.fetch_results(sim, True) |
|
|
| |
| if cnt % 1000 == 0: |
| goal_quat = random_quaternion() |
|
|
| print("New goal orientation:", goal_quat) |
|
|
| gym.clear_lines(viewer) |
|
|
| goal_viz_T = gymapi.Transform(r=gymapi.Quat(*goal_quat)) |
| gymutil.draw_lines(axes_geom, gym, viewer, env, goal_viz_T) |
|
|
| dof_positions[:] = 0.0 |
| dof_positions[3:] = quat2expcoord(goal_quat) |
|
|
| for i in range(num_envs): |
| gym.set_actor_dof_position_targets(envs[i], actor_handles[i], dof_positions) |
|
|
| |
| gym.step_graphics(sim) |
| gym.draw_viewer(viewer, sim, True) |
|
|
| |
| |
| gym.sync_frame_time(sim) |
|
|
| cnt += 1 |
|
|
| print("Done") |
|
|
| gym.destroy_viewer(viewer) |
| gym.destroy_sim(sim) |
|
|