.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/simulator/example_curi_cube_ik_osc.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_simulator_example_curi_cube_ik_osc.py: CURI cube pick ============== Use Jacobian matrix and inverse kinematics control of curi robot to pick up a box. Damped Least Squares method from: https://www.math.ucsd.edu/~sbuss/ResearchWeb/ikmethods/iksurvey.pdf .. GENERATED FROM PYTHON SOURCE LINES 8-423 .. code-block:: default from isaacgym import gymapi from isaacgym import gymutil from isaacgym import gymtorch from isaacgym.torch_utils import * import math import numpy as np import torch import random import time DOF = 25 def quat_axis(q, axis=0): basis_vec = torch.zeros(q.shape[0], 3, device=q.device) basis_vec[:, axis] = 1 return quat_rotate(q, basis_vec) def orientation_error(desired, current): cc = quat_conjugate(current) q_r = quat_mul(desired, cc) return q_r[:, 0:3] * torch.sign(q_r[:, 3]).unsqueeze(-1) def cube_grasping_yaw(q, corners): """ returns horizontal rotation required to grasp cube """ rc = quat_rotate(q, corners) yaw = (torch.atan2(rc[:, 1], rc[:, 0]) - 0.25 * math.pi) % (0.5 * math.pi) theta = 0.5 * yaw w = theta.cos() x = torch.zeros_like(w) y = torch.zeros_like(w) z = theta.sin() yaw_quats = torch.stack([x, y, z, w], dim=-1) return yaw_quats def control_ik(dpose): global damping, j_eef, num_envs # solve damped least squares j_eef_T = torch.transpose(j_eef, 1, 2) lmbda = torch.eye(6, device=device) * (damping ** 2) u = (j_eef_T @ torch.inverse(j_eef @ j_eef_T + lmbda) @ dpose).view(num_envs, 7) return u def control_osc(dpose): global kp, kd, kp_null, kd_null, default_dof_pos_tensor, mm, j_eef, num_envs, dof_pos, dof_vel, hand_vel mm_inv = torch.inverse(mm) m_eef_inv = j_eef @ mm_inv @ torch.transpose(j_eef, 1, 2) m_eef = torch.inverse(m_eef_inv) u = torch.transpose(j_eef, 1, 2) @ m_eef @ ( kp * dpose - kd * hand_vel.unsqueeze(-1)) # Nullspace control torques `u_null` prevents large changes in joint configuration # They are added into the nullspace of OSC so that the end effector orientation remains constant # roboticsproceedings.org/rss07/p31.pdf j_eef_inv = m_eef @ j_eef @ mm_inv u_null = kd_null * -dof_vel + kp_null * ( (default_dof_pos_tensor.view(1, -1, 1) - dof_pos + np.pi) % (2 * np.pi) - np.pi) u_null = u_null[:, 7:14] u_null = mm @ u_null u += (torch.eye(7, device=device).unsqueeze(0) - torch.transpose(j_eef, 1, 2) @ j_eef_inv) @ u_null return u.squeeze(-1) # set random seed np.random.seed(42) torch.set_printoptions(precision=4, sci_mode=False) # acquire gym interface gym = gymapi.acquire_gym() # parse arguments # Add custom arguments custom_parameters = [ {"name": "--controller", "type": str, "default": "osc", "help": "Controller to use for curi. Options are {ik, osc}"}, {"name": "--num_envs", "type": int, "default": 1, "help": "Number of environments to create"}, ] args = gymutil.parse_arguments( description="curi Jacobian Inverse Kinematics (IK) + Operational Space Control (OSC) Example", custom_parameters=custom_parameters, ) # Grab controller controller = args.controller assert controller in {"ik", "osc"}, f"Invalid controller specified -- options are (ik, osc). Got: {controller}" # set torch device device = args.sim_device if args.use_gpu_pipeline else 'cpu' # configure sim sim_params = gymapi.SimParams() sim_params.up_axis = gymapi.UP_AXIS_Z sim_params.gravity = gymapi.Vec3(0.0, 0.0, -9.8) sim_params.dt = 1.0 / 60.0 sim_params.substeps = 2 sim_params.use_gpu_pipeline = args.use_gpu_pipeline if args.physics_engine == gymapi.SIM_PHYSX: sim_params.physx.solver_type = 1 sim_params.physx.num_position_iterations = 8 sim_params.physx.num_velocity_iterations = 1 sim_params.physx.rest_offset = 0.0 sim_params.physx.contact_offset = 0.001 sim_params.physx.friction_offset_threshold = 0.001 sim_params.physx.friction_correlation_distance = 0.0005 sim_params.physx.num_threads = args.num_threads sim_params.physx.use_gpu = args.use_gpu else: raise Exception("This example can only be used with PhysX") # Set controller parameters # IK params damping = 0.05 # OSC params kp = 150. kd = 2.0 * np.sqrt(kp) kp_null = 10. kd_null = 2.0 * np.sqrt(kp_null) # create sim sim = gym.create_sim(args.compute_device_id, args.graphics_device_id, args.physics_engine, sim_params) if sim is None: raise Exception("Failed to create sim") # create viewer viewer = gym.create_viewer(sim, gymapi.CameraProperties()) if viewer is None: raise Exception("Failed to create viewer") asset_root = "../assets" # create table asset table_dims = gymapi.Vec3(0.6, 2.5, 0.5) asset_options = gymapi.AssetOptions() asset_options.fix_base_link = True table_asset = gym.create_box(sim, table_dims.x, table_dims.y, table_dims.z, asset_options) # create box asset box_size = 0.045 asset_options = gymapi.AssetOptions() box_asset = gym.create_box(sim, box_size, box_size, box_size, asset_options) # load curi asset curi_asset_file = "urdf/curi/urdf/curi_isaacgym.urdf" asset_options = gymapi.AssetOptions() asset_options.armature = 0.01 asset_options.fix_base_link = True asset_options.disable_gravity = True asset_options.flip_visual_attachments = True curi_asset = gym.load_asset(sim, asset_root, curi_asset_file, asset_options) # configure curi dofs curi_dof_props = gym.get_asset_dof_properties(curi_asset) curi_lower_limits = curi_dof_props["lower"] curi_upper_limits = curi_dof_props["upper"] curi_ranges = curi_upper_limits - curi_lower_limits curi_mids = 0.3 * (curi_upper_limits + curi_lower_limits) # use position drive for all dofs if controller == "ik": curi_dof_props["driveMode"][7:].fill(gymapi.DOF_MODE_POS) curi_dof_props["stiffness"][7:].fill(300.0) curi_dof_props["damping"][7:].fill(80.0) else: # osc curi_dof_props["driveMode"][7:].fill(gymapi.DOF_MODE_EFFORT) curi_dof_props["stiffness"][7:].fill(0.0) curi_dof_props["damping"][7:].fill(0.0) # grippers curi_dof_props["driveMode"][14:16].fill(gymapi.DOF_MODE_POS) curi_dof_props["stiffness"][14:16].fill(800.0) curi_dof_props["damping"][14:16].fill(40.0) curi_dof_props["driveMode"][23:25].fill(gymapi.DOF_MODE_POS) curi_dof_props["stiffness"][23:25].fill(800.0) curi_dof_props["damping"][23:25].fill(40.0) # default dof states and position targets curi_num_dofs = gym.get_asset_dof_count(curi_asset) default_dof_pos = np.zeros(curi_num_dofs, dtype=np.float32) default_dof_pos[7:] = curi_mids[7:] # grippers open default_dof_pos[14:16] = curi_upper_limits[14:16] default_dof_pos[23:25] = curi_upper_limits[23:25] default_dof_state = np.zeros(curi_num_dofs, gymapi.DofState.dtype) default_dof_state["pos"] = default_dof_pos # send to torch default_dof_pos_tensor = to_torch(default_dof_pos, device=device) # get link index of panda hand, which we will use as end effector curi_link_dict = gym.get_asset_rigid_body_dict(curi_asset) curi_hand_index = curi_link_dict["panda_left_hand"] # configure env grid num_envs = args.num_envs num_per_row = int(math.sqrt(num_envs)) spacing = 1.0 env_lower = gymapi.Vec3(-spacing, -spacing, 0.0) env_upper = gymapi.Vec3(spacing, spacing, spacing) print("Creating %d environments" % num_envs) curi_pose = gymapi.Transform() curi_pose.p = gymapi.Vec3(0, 0, 0) table_pose = gymapi.Transform() table_pose.p = gymapi.Vec3(1, 0.0, 0.5 * table_dims.z) box_pose = gymapi.Transform() envs = [] box_idxs = [] hand_idxs = [] init_pos_list = [] init_rot_list = [] # add ground plane plane_params = gymapi.PlaneParams() plane_params.normal = gymapi.Vec3(0, 0, 1) gym.add_ground(sim, plane_params) for i in range(num_envs): # create env env = gym.create_env(sim, env_lower, env_upper, num_per_row) envs.append(env) # add table table_handle = gym.create_actor(env, table_asset, table_pose, "table", i, 0) # add box box_pose.p.x = table_pose.p.x + np.random.uniform(-0.2, 0.1) box_pose.p.y = table_pose.p.y + np.random.uniform(-0.3, 0.3) box_pose.p.z = table_dims.z + 0.5 * box_size box_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), np.random.uniform(-math.pi, math.pi)) box_handle = gym.create_actor(env, box_asset, box_pose, "box", i, 0) color = gymapi.Vec3(np.random.uniform(0, 1), np.random.uniform(0, 1), np.random.uniform(0, 1)) gym.set_rigid_body_color(env, box_handle, 0, gymapi.MESH_VISUAL_AND_COLLISION, color) # get global index of box in rigid body state tensor box_idx = gym.get_actor_rigid_body_index(env, box_handle, 0, gymapi.DOMAIN_SIM) box_idxs.append(box_idx) # add curi curi_handle = gym.create_actor(env, curi_asset, curi_pose, "curi", i, 2) # set dof properties gym.set_actor_dof_properties(env, curi_handle, curi_dof_props) # set initial dof states gym.set_actor_dof_states(env, curi_handle, default_dof_state, gymapi.STATE_ALL) # set initial position targets gym.set_actor_dof_position_targets(env, curi_handle, default_dof_pos) # get inital hand pose hand_handle = gym.find_actor_rigid_body_handle(env, curi_handle, "panda_left_hand") hand_pose = gym.get_rigid_transform(env, hand_handle) init_pos_list.append([hand_pose.p.x, hand_pose.p.y, hand_pose.p.z]) init_rot_list.append([hand_pose.r.x, hand_pose.r.y, hand_pose.r.z, hand_pose.r.w]) # get global index of hand in rigid body state tensor hand_idx = gym.find_actor_rigid_body_index(env, curi_handle, "panda_left_hand", gymapi.DOMAIN_SIM) hand_idxs.append(hand_idx) # point camera at middle env cam_pos = gymapi.Vec3(4, 3, 2) cam_target = gymapi.Vec3(-4, -3, 0) middle_env = envs[num_envs // 2 + num_per_row // 2] gym.viewer_camera_look_at(viewer, middle_env, cam_pos, cam_target) # ==== prepare tensors ===== # from now on, we will use the tensor API that can run on CPU or GPU gym.prepare_sim(sim) # initial hand position and orientation tensors init_pos = torch.Tensor(init_pos_list).view(num_envs, 3).to(device) init_rot = torch.Tensor(init_rot_list).view(num_envs, 4).to(device) # hand orientation for grasping down_q = torch.stack(num_envs * [torch.tensor([1.0, 0.0, 0.0, 0.0])]).to(device).view((num_envs, 4)) # box corner coords, used to determine grasping yaw box_half_size = 0.5 * box_size corner_coord = torch.Tensor([box_half_size, box_half_size, box_half_size]) corners = torch.stack(num_envs * [corner_coord]).to(device) # downard axis down_dir = torch.Tensor([0, 0, -1]).to(device).view(1, 3) # get jacobian tensor # for fixed-base curi, tensor has shape (num envs, 10, 6, 9) _jacobian = gym.acquire_jacobian_tensor(sim, "curi") jacobian = gymtorch.wrap_tensor(_jacobian) # jacobian entries corresponding to curi hand j_eef = jacobian[:, curi_hand_index - 1, :, 7:14] # get mass matrix tensor _massmatrix = gym.acquire_mass_matrix_tensor(sim, "curi") mm = gymtorch.wrap_tensor(_massmatrix) mm = mm[:, 7:14, 7:14] # only need elements corresponding to the curi arm # get rigid body state tensor _rb_states = gym.acquire_rigid_body_state_tensor(sim) rb_states = gymtorch.wrap_tensor(_rb_states) # get dof state tensor _dof_states = gym.acquire_dof_state_tensor(sim) dof_states = gymtorch.wrap_tensor(_dof_states) dof_pos = dof_states[:, 0].view(num_envs, DOF, 1) dof_vel = dof_states[:, 1].view(num_envs, DOF, 1) # Create a tensor noting whether the hand should return to the initial position hand_restart = torch.full([num_envs], False, dtype=torch.bool).to(device) # Set action tensors pos_action = torch.zeros_like(dof_pos).squeeze(-1) effort_action = torch.zeros_like(pos_action) # simulation loop while not gym.query_viewer_has_closed(viewer): # step the physics gym.simulate(sim) gym.fetch_results(sim, True) # refresh tensors gym.refresh_rigid_body_state_tensor(sim) gym.refresh_dof_state_tensor(sim) gym.refresh_jacobian_tensors(sim) gym.refresh_mass_matrix_tensors(sim) box_pos = rb_states[box_idxs, :3] box_rot = rb_states[box_idxs, 3:7] hand_pos = rb_states[hand_idxs, :3] hand_rot = rb_states[hand_idxs, 3:7] hand_vel = rb_states[hand_idxs, 7:] to_box = box_pos - hand_pos box_dist = torch.norm(to_box, dim=-1).unsqueeze(-1) box_dir = to_box / box_dist box_dot = box_dir @ down_dir.view(3, 1) # how far the hand should be from box for grasping grasp_offset = 0.11 if controller == "ik" else 0.10 # determine if we're holding the box (grippers are closed and box is near) gripper_sep = dof_pos[:, 14] + dof_pos[:, 15] gripped = (gripper_sep < 0.045) & (box_dist < grasp_offset + 0.5 * box_size) yaw_q = cube_grasping_yaw(box_rot, corners) box_yaw_dir = quat_axis(yaw_q, 0) hand_yaw_dir = quat_axis(hand_rot, 0) yaw_dot = torch.bmm(box_yaw_dir.view(num_envs, 1, 3), hand_yaw_dir.view(num_envs, 3, 1)).squeeze(-1) # determine if we have reached the initial position; if so allow the hand to start moving to the box to_init = init_pos - hand_pos init_dist = torch.norm(to_init, dim=-1) hand_restart = (hand_restart & (init_dist > 0.02)).squeeze(-1) return_to_start = (hand_restart | gripped.squeeze(-1)).unsqueeze(-1) # if hand is above box, descend to grasp offset # otherwise, seek a position above the box above_box = ((box_dot >= 0.99) & (yaw_dot >= 0.95) & (box_dist < grasp_offset * 3)).squeeze(-1) grasp_pos = box_pos.clone() grasp_pos[:, 2] = torch.where(above_box, box_pos[:, 2] + grasp_offset, box_pos[:, 2] + grasp_offset * 2.5) # compute goal position and orientation goal_pos = torch.where(return_to_start, init_pos, grasp_pos) goal_rot = torch.where(return_to_start, init_rot, quat_mul(down_q, quat_conjugate(yaw_q))) # compute position and orientation error pos_err = goal_pos - hand_pos orn_err = orientation_error(goal_rot, hand_rot) dpose = torch.cat([pos_err, orn_err], -1).unsqueeze(-1) import rofunc as rf rf.logger.beauty_print("pos_err: {}".format(pos_err), type="info") rf.logger.beauty_print("orn_err: {}".format(orn_err), type="info") # Deploy control based on type if controller == "ik": pos_action[:, 7:14] = dof_pos.squeeze(-1)[:, 7:14] + control_ik(dpose) else: # osc effort_action[:, 7:14] = control_osc(dpose) # gripper actions depend on distance between hand and box close_gripper = (box_dist < grasp_offset + 0.02) | gripped # always open the gripper above a certain height, dropping the box and restarting from the beginning hand_restart = hand_restart | (box_pos[:, 2] > 0.6) keep_going = torch.logical_not(hand_restart) close_gripper = close_gripper & keep_going.unsqueeze(-1) grip_acts = torch.where(close_gripper, torch.Tensor([[0., 0.]] * num_envs).to(device), torch.Tensor([[0.04, 0.04]] * num_envs).to(device)) pos_action[:, 14:16] = grip_acts # Deploy actions gym.set_dof_position_target_tensor(sim, gymtorch.unwrap_tensor(pos_action)) gym.set_dof_actuation_force_tensor(sim, gymtorch.unwrap_tensor(effort_action)) # update viewer gym.step_graphics(sim) gym.draw_viewer(viewer, sim, False) gym.sync_frame_time(sim) # cleanup gym.destroy_viewer(viewer) gym.destroy_sim(sim) .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.000 seconds) .. _sphx_glr_download_examples_simulator_example_curi_cube_ik_osc.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: example_curi_cube_ik_osc.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: example_curi_cube_ik_osc.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_