.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples/simulator/example_curi_nut_bolt_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_nut_bolt_ik_osc.py: CURI screw nut ================= This example shows how to use the gym interface to control the CURI robot to screw a nut onto a bolt. .. GENERATED FROM PYTHON SOURCE LINES 7-516 .. code-block:: default import os.path 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 rofunc as rf DOF = 18 # Gripper_index = [14, 15, 23, 24] Gripper_index = [7, 8, 16, 17] # Arm_joint_index = [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24] Arm_joint_index = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] 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]) def control_ik(dpose, 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 class ScrewFSM: def __init__(self, sim_dt, nut_height, bolt_height, screw_speed, screw_limit_angle, device, env_idx): self._sim_dt = sim_dt self._nut_height = nut_height self._bolt_height = bolt_height self._screw_speed = screw_speed self._screw_limit_angle = screw_limit_angle self.device = device self.env_idx = env_idx # states: self._state = "go_above_nut" # control / position constants: self._above_offset = torch.tensor([0, 0, 0.08 + self._bolt_height], dtype=torch.float32, device=self.device) self._grip_offset = torch.tensor([0, 0, 0.12 + self._nut_height], dtype=torch.float32, device=self.device) self._lift_offset = torch.tensor([0, 0, 0.25 + self._bolt_height], dtype=torch.float32, device=self.device) self._above_bolt_offset = torch.tensor([0, 0, self._bolt_height], dtype=torch.float32, device=self.device) + self._grip_offset self._on_bolt_offset = torch.tensor([0, 0, 0.8 * self._bolt_height], dtype=torch.float32, device=self.device) + self._grip_offset self._hand_down_quat = torch.tensor([1, 0, 0, 0], dtype=torch.float32, device=self.device) grab_angle = torch.tensor([np.pi / 6.0], dtype=torch.float32, device=self.device) grab_axis = torch.tensor([0, 0, 1], dtype=torch.float32, device=self.device) grab_quat = quat_from_angle_axis(grab_angle, grab_axis).squeeze() self._nut_grab_q = quat_mul(grab_quat, self._hand_down_quat) self._screw_angle = torch.tensor([0.0], dtype=torch.float32, device=self.device) self._screw_axis = torch.tensor([0, 0, 1], dtype=torch.float32, device=self.device) self._dpose = torch.zeros(6, dtype=torch.float32, device=self.device) self._gripper_separation = 0.0 def get_dp_from_target(self, target_pos, target_quat, hand_pose) -> float: self._dpose[:3] = target_pos - hand_pose[:3] self._dpose[3:] = orientation_error(target_quat, hand_pose[3:]) return torch.norm(self._dpose, p=2) # returns def update(self, nut_pose, bolt_pose, hand_pose, current_gripper_sep): newState = self._state if self._state == "go_above_nut": self._gripper_separation = 0.08 target_pos = nut_pose[:3] + self._above_offset error = self.get_dp_from_target(target_pos, self._hand_down_quat, hand_pose) if error < 2e-3: newState = "prep_grip" elif self._state == "prep_grip": pass self._gripper_separation = 0.08 target_pos = nut_pose[:3] + self._grip_offset targetQ = quat_mul(nut_pose[3:], self._nut_grab_q) error = self.get_dp_from_target(target_pos, targetQ, hand_pose) if error < 2e-3: newState = "grip" elif self._state == "grip": self._gripper_separation = 0.0 target_pos = nut_pose[:3] + self._grip_offset targetQ = quat_mul(nut_pose[3:], self._nut_grab_q) error = self.get_dp_from_target(target_pos, targetQ, hand_pose) gripped = (current_gripper_sep < 0.035) if error < 1e-2 and gripped: newState = "lift" elif self._state == "lift": self._gripper_separation = 0.0 target_pos = nut_pose[:3] target_pos[2] = bolt_pose[2] + 0.004 target_pos = target_pos + self._lift_offset error = self.get_dp_from_target(target_pos, self._hand_down_quat, hand_pose) if error < 2e-3: newState = "go_above_bolt" elif self._state == "go_above_bolt": self._gripper_separation = 0.0 target_pos = bolt_pose[:3] target_pos = target_pos + self._above_bolt_offset error = self.get_dp_from_target(target_pos, self._hand_down_quat, hand_pose) if error < 2e-3: newState = "go_on_bolt" elif self._state == "go_on_bolt": self._gripper_separation = 0.0 target_pos = bolt_pose[:3] target_pos[2] = bolt_pose[2] target_pos = target_pos + self._on_bolt_offset error = self.get_dp_from_target(target_pos, self._hand_down_quat, hand_pose) if error < 2e-3: newState = "loosen_grip" elif self._state == "loosen_grip": target_sep = 0.037 self._gripper_separation = target_sep target_pos = bolt_pose[:3] target_pos = target_pos + self._on_bolt_offset error = self.get_dp_from_target(target_pos, self._hand_down_quat, hand_pose) un_gripped = current_gripper_sep > target_sep * 0.98 if error < 2e-3 and un_gripped: self._screw_angle[0] = 0.0 newState = "screw_motion" elif self._state == "screw_motion": target_sep = 0.037 self._gripper_separation = target_sep target_pos = bolt_pose[:3] target_pos[2] = nut_pose[2] target_pos = target_pos + self._grip_offset self._screw_angle[0] = self._screw_angle[0] - self._sim_dt * self._screw_speed screw_quat = quat_from_angle_axis(self._screw_angle, self._screw_axis).squeeze() self.get_dp_from_target(target_pos, quat_mul(screw_quat, self._hand_down_quat), hand_pose) if self._screw_angle[0] < -self._screw_limit_angle: newState = "ungrip_screw" elif self._state == "ungrip_screw": target_sep = 0.06 self._gripper_separation = target_sep target_pos = bolt_pose[:3] target_pos[2] = nut_pose[2] target_pos = target_pos + self._grip_offset screw_quat = quat_from_angle_axis(self._screw_angle, self._screw_axis).squeeze() self.get_dp_from_target(target_pos, quat_mul(screw_quat, self._hand_down_quat), hand_pose) un_gripped = current_gripper_sep > target_sep * 0.98 if un_gripped: newState = "rotate_back" elif self._state == "rotate_back": target_sep = 0.06 self._gripper_separation = target_sep target_pos = bolt_pose[:3] target_pos[2] = nut_pose[2] target_pos = target_pos + self._grip_offset self._screw_angle[0] = self._screw_angle[0] + self._sim_dt * 2.0 * self._screw_speed screw_quat = quat_from_angle_axis(self._screw_angle, self._screw_axis).squeeze() self.get_dp_from_target(target_pos, quat_mul(screw_quat, self._hand_down_quat), hand_pose) if self._screw_angle[0] > 0.99 * self._screw_limit_angle: newState = "back_to_screw_grip" elif self._state == "back_to_screw_grip": target_sep = 0.04 self._gripper_separation = target_sep target_pos = bolt_pose[:3] target_pos[2] = nut_pose[2] target_pos = target_pos + self._grip_offset screw_quat = quat_from_angle_axis(self._screw_angle, self._screw_axis).squeeze() error = self.get_dp_from_target(target_pos, quat_mul(screw_quat, self._hand_down_quat), hand_pose) gripped = (current_gripper_sep < target_sep * 1.01) if error < 2e-3 and gripped: self._screw_angle[0] = self._screw_limit_angle newState = "screw_motion" if newState != self._state: self._state = newState print(f"Env {self.env_idx} going to state {newState}") @property def d_pose(self): return self._dpose @property def gripper_separation(self): return self._gripper_separation # 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": "--num_envs", "type": int, "default": 1, "help": "Number of environments to create"}, ] args = gymutil.parse_arguments( description="curi Jacobian Inverse Kinematics (IK) Nut-Bolt Screwing", custom_parameters=custom_parameters, ) # Force GPU: if not args.use_gpu or args.use_gpu_pipeline: print("Forcing GPU sim - CPU sim not supported by SDF") args.use_gpu = True args.use_gpu_pipeline = True # set torch device device = args.sim_device # 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 = 32 sim_params.physx.num_velocity_iterations = 1 sim_params.physx.rest_offset = 0.0 sim_params.physx.contact_offset = 0.005 sim_params.physx.friction_offset_threshold = 0.01 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 = 0.1 # 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") rofunc_path = rf.oslab.get_rofunc_path() asset_root = os.path.join(rofunc_path, "simulator/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 bolt asset bolt_file = "urdf/nut_bolt/bolt_m4_tight_SI_5x.urdf" bolt_options = gymapi.AssetOptions() bolt_options.flip_visual_attachments = False # default = False bolt_options.fix_base_link = True bolt_options.thickness = 0.0 # default = 0.02 (not overridden in .cpp) bolt_options.density = 800.0 # 7850.0 bolt_options.armature = 0.0 # default = 0.0 bolt_options.linear_damping = 0.0 # default = 0.0 bolt_options.max_linear_velocity = 1000.0 # default = 1000.0 bolt_options.angular_damping = 0.0 # default = 0.5 bolt_options.max_angular_velocity = 1000.0 # default = 64.0 bolt_options.disable_gravity = False # default = False bolt_options.enable_gyroscopic_forces = True # default = True bolt_asset = gym.load_asset(sim, asset_root, bolt_file, bolt_options) # create nut asset nut_file = "urdf/nut_bolt/nut_m4_tight_SI_5x.urdf" nut_options = gymapi.AssetOptions() nut_options.flip_visual_attachments = False # default = False nut_options.fix_base_link = False nut_options.thickness = 0.0 # default = 0.02 (not overridden in .cpp) nut_options.density = 800 # 7850.0 # default = 1000 nut_options.armature = 0.0 # default = 0.0 nut_options.linear_damping = 0.0 # default = 0.0 nut_options.max_linear_velocity = 1000.0 # default = 1000.0 nut_options.angular_damping = 0.0 # default = 0.5 nut_options.max_angular_velocity = 1000.0 # default = 64.0 nut_options.disable_gravity = False # default = False nut_options.enable_gyroscopic_forces = True # default = True nut_asset = gym.load_asset(sim, asset_root, nut_file, nut_options) # create box asset # 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_dual_arm.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 curi_dof_props["driveMode"][:].fill(gymapi.DOF_MODE_POS) curi_dof_props["stiffness"][:].fill(400.0) curi_dof_props["damping"][:].fill(40.0) # grippers curi_dof_props["driveMode"][7:9].fill(gymapi.DOF_MODE_POS) curi_dof_props["stiffness"][7:9].fill(800.0) curi_dof_props["damping"][7:9].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[:] = curi_mids[:] # grippers open default_dof_pos[7:9] = curi_upper_limits[7:9] 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) bolt_pose = gymapi.Transform() nut_pose = gymapi.Transform() # fsm parameters: fsm_device = 'cpu' envs = [] nut_idxs = [] bolt_idxs = [] hand_idxs = [] fsms = [] # 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 bolt bolt_pose.p.x = table_pose.p.x + np.random.uniform(-0.1, 0.1) bolt_pose.p.y = table_pose.p.y + np.random.uniform(-0.3, 0.0) bolt_pose.p.z = table_dims.z bolt_pose.r = gymapi.Quat.from_axis_angle(gymapi.Vec3(0, 0, 1), np.random.uniform(-math.pi, math.pi)) bolt_handle = gym.create_actor(env, bolt_asset, bolt_pose, "bolt", i, 0) bolt_props = gym.get_actor_rigid_shape_properties(env, bolt_handle) # bolt_props[0].filter = imesh bolt_props[0].friction = 0.0 # default = ? bolt_props[0].rolling_friction = 0.0 # default = 0.0 bolt_props[0].torsion_friction = 0.0 # default = 0.0 bolt_props[0].restitution = 0.0 # default = ? bolt_props[0].compliance = 0.0 # default = 0.0 bolt_props[0].thickness = 0.0 # default = 0.0 gym.set_actor_rigid_shape_properties(env, bolt_handle, bolt_props) # get global index of box in rigid body state tensor bolt_idx = gym.get_actor_rigid_body_index(env, bolt_handle, 0, gymapi.DOMAIN_SIM) bolt_idxs.append(bolt_idx) # add nut nut_pose.p.x = bolt_pose.p.x + np.random.uniform(-0.04, 0.04) nut_pose.p.y = bolt_pose.p.y + 0.2 + np.random.uniform(-0.04, 0.04) nut_pose.p.z = table_dims.z + 0.02 nut_handle = gym.create_actor(env, nut_asset, nut_pose, "nut", i, 0) nut_props = gym.get_actor_rigid_shape_properties(env, nut_handle) # nut_props[0].filter = i nut_props[0].friction = 0.2 # default = ? nut_props[0].rolling_friction = 0.0 # default = 0.0 nut_props[0].torsion_friction = 0.0 # default = 0.0 nut_props[0].restitution = 0.0 # default = ? nut_props[0].compliance = 0.0 # default = 0.0 nut_props[0].thickness = 0.0 # default = 0.0 gym.set_actor_rigid_shape_properties(env, nut_handle, nut_props) # get global index of box in rigid body state tensor nut_idx = gym.get_actor_rigid_body_index(env, nut_handle, 0, gymapi.DOMAIN_SIM) nut_idxs.append(nut_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 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) # create env's fsm - run them on CPU fsms.append(ScrewFSM(sim_params.dt, 0.016, 0.1, 30.0 / 180.0 * np.pi, 60.0 / 180.0 * np.pi, fsm_device, i)) # point camera at middle env cam_pos = gymapi.Vec3(1, 0, 0.6) cam_target = gymapi.Vec3(-1, 0, 0.5) middle_env = envs[0] 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) # 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] # 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) # Set action tensors pos_action = torch.zeros_like(dof_pos).squeeze(-1) # dp and gripper sep tensors: d_pose = torch.zeros((num_envs, 6), dtype=torch.float32, device=fsm_device) grip_sep = torch.zeros((num_envs, 1), dtype=torch.float32, device=fsm_device) # 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) rb_states_fsm = rb_states.to(fsm_device) nut_poses = rb_states_fsm[nut_idxs, :7] bolt_poses = rb_states_fsm[bolt_idxs, :7] hand_poses = rb_states_fsm[hand_idxs, :7] dof_pos_fsm = dof_pos.to(fsm_device) cur_grip_sep_fsm = dof_pos_fsm[:, 7] + dof_pos_fsm[:, 8] for env_idx in range(num_envs): fsms[env_idx].update(nut_poses[env_idx, :], bolt_poses[env_idx, :], hand_poses[env_idx, :], cur_grip_sep_fsm[env_idx]) d_pose[env_idx, :] = fsms[env_idx].d_pose grip_sep[env_idx] = fsms[env_idx].gripper_separation pos_action[:, :7] = dof_pos.squeeze(-1)[:, :7] + control_ik(d_pose.unsqueeze(-1).to(device), damping, j_eef, num_envs) # gripper actions depend on distance between hand and box grip_acts = torch.cat((0.5 * grip_sep, 0.5 * grip_sep), 1).to(device) pos_action[:, 7:9] = grip_acts # Deploy actions gym.set_dof_position_target_tensor(sim, gymtorch.unwrap_tensor(pos_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_nut_bolt_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_nut_bolt_ik_osc.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: example_curi_nut_bolt_ik_osc.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_