Using a task-space controller — Isaac Lab Documentation (original) (raw)

Using a task-space controller#

In the previous tutorials, we have joint-space controllers to control the robot. However, in many cases, it is more intuitive to control the robot using a task-space controller. For example, if we want to teleoperate the robot, it is easier to specify the desired end-effector pose rather than the desired joint positions.

In this tutorial, we will learn how to use a task-space controller to control the robot. We will use the controllers.DifferentialIKController class to track a desired end-effector pose command.

The Code#

The tutorial corresponds to the run_diff_ik.py script in thescripts/tutorials/05_controllers directory.

1# Copyright (c) 2022-2025, The Isaac Lab Project Developers. 2# All rights reserved. 3# 4# SPDX-License-Identifier: BSD-3-Clause 5 6""" 7This script demonstrates how to use the differential inverse kinematics controller with the simulator. 8 9The differential IK controller can be configured in different modes. It uses the Jacobians computed by 10PhysX. This helps perform parallelized computation of the inverse kinematics. 11 12.. code-block:: bash 13 14 # Usage 15 ./isaaclab.sh -p scripts/tutorials/05_controllers/run_diff_ik.py 16 17""" 18 19"""Launch Isaac Sim Simulator first.""" 20 21import argparse 22 23from isaaclab.app import AppLauncher 24 25# add argparse arguments 26parser = argparse.ArgumentParser(description="Tutorial on using the differential IK controller.") 27parser.add_argument("--robot", type=str, default="franka_panda", help="Name of the robot.") 28parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to spawn.") 29# append AppLauncher cli args 30AppLauncher.add_app_launcher_args(parser) 31# parse the arguments 32args_cli = parser.parse_args() 33 34# launch omniverse app 35app_launcher = AppLauncher(args_cli) 36simulation_app = app_launcher.app 37 38"""Rest everything follows.""" 39 40import torch 41 42import isaaclab.sim as sim_utils 43from isaaclab.assets import AssetBaseCfg 44from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg 45from isaaclab.managers import SceneEntityCfg 46from isaaclab.markers import VisualizationMarkers 47from isaaclab.markers.config import FRAME_MARKER_CFG 48from isaaclab.scene import InteractiveScene, InteractiveSceneCfg 49from isaaclab.utils import configclass 50from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR 51from isaaclab.utils.math import subtract_frame_transforms 52 53## 54# Pre-defined configs 55## 56from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG # isort:skip 57 58 59@configclass 60class TableTopSceneCfg(InteractiveSceneCfg): 61 """Configuration for a cart-pole scene.""" 62 63 # ground plane 64 ground = AssetBaseCfg( 65 prim_path="/World/defaultGroundPlane", 66 spawn=sim_utils.GroundPlaneCfg(), 67 init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)), 68 ) 69 70 # lights 71 dome_light = AssetBaseCfg( 72 prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75)) 73 ) 74 75 # mount 76 table = AssetBaseCfg( 77 prim_path="{ENV_REGEX_NS}/Table", 78 spawn=sim_utils.UsdFileCfg( 79 usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0) 80 ), 81 ) 82 83 # articulation 84 if args_cli.robot == "franka_panda": 85 robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") 86 elif args_cli.robot == "ur10": 87 robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot") 88 else: 89 raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") 90 91 92def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene): 93 """Runs the simulation loop.""" 94 # Extract scene entities 95 # note: we only do this here for readability. 96 robot = scene["robot"] 97 98 # Create controller 99 diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls") 100 diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device) 101 102 # Markers 103 frame_marker_cfg = FRAME_MARKER_CFG.copy() 104 frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1) 105 ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current")) 106 goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal")) 107 108 # Define goals for the arm 109 ee_goals = [ 110 [0.5, 0.5, 0.7, 0.707, 0, 0.707, 0], 111 [0.5, -0.4, 0.6, 0.707, 0.707, 0.0, 0.0], 112 [0.5, 0, 0.5, 0.0, 1.0, 0.0, 0.0], 113 ] 114 ee_goals = torch.tensor(ee_goals, device=sim.device) 115 # Track the given command 116 current_goal_idx = 0 117 # Create buffers to store actions 118 ik_commands = torch.zeros(scene.num_envs, diff_ik_controller.action_dim, device=robot.device) 119 ik_commands[:] = ee_goals[current_goal_idx] 120 121 # Specify robot-specific parameters 122 if args_cli.robot == "franka_panda": 123 robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint."], body_names=["panda_hand"]) 124 elif args_cli.robot == "ur10": 125 robot_entity_cfg = SceneEntityCfg("robot", joint_names=["."], body_names=["ee_link"]) 126 else: 127 raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10") 128 # Resolving the scene entities 129 robot_entity_cfg.resolve(scene) 130 # Obtain the frame index of the end-effector 131 # For a fixed base robot, the frame index is one less than the body index. This is because 132 # the root body is not included in the returned Jacobians. 133 if robot.is_fixed_base: 134 ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1 135 else: 136 ee_jacobi_idx = robot_entity_cfg.body_ids[0] 137 138 # Define simulation stepping 139 sim_dt = sim.get_physics_dt() 140 count = 0 141 # Simulation loop 142 while simulation_app.is_running(): 143 # reset 144 if count % 150 == 0: 145 # reset time 146 count = 0 147 # reset joint state 148 joint_pos = robot.data.default_joint_pos.clone() 149 joint_vel = robot.data.default_joint_vel.clone() 150 robot.write_joint_state_to_sim(joint_pos, joint_vel) 151 robot.reset() 152 # reset actions 153 ik_commands[:] = ee_goals[current_goal_idx] 154 joint_pos_des = joint_pos[:, robot_entity_cfg.joint_ids].clone() 155 # reset controller 156 diff_ik_controller.reset() 157 diff_ik_controller.set_command(ik_commands) 158 # change goal 159 current_goal_idx = (current_goal_idx + 1) % len(ee_goals) 160 else: 161 # obtain quantities from simulation 162 jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids] 163 ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7] 164 root_pose_w = robot.data.root_state_w[:, 0:7] 165 joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids] 166 # compute frame in root frame 167 ee_pos_b, ee_quat_b = subtract_frame_transforms( 168 root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7] 169 ) 170 # compute the joint commands 171 joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos) 172 173 # apply actions 174 robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids) 175 scene.write_data_to_sim() 176 # perform step 177 sim.step() 178 # update sim-time 179 count += 1 180 # update buffers 181 scene.update(sim_dt) 182 183 # obtain quantities from simulation 184 ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7] 185 # update marker positions 186 ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]) 187 goal_marker.visualize(ik_commands[:, 0:3] + scene.env_origins, ik_commands[:, 3:7]) 188 189 190def main(): 191 """Main function.""" 192 # Load kit helper 193 sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device) 194 sim = sim_utils.SimulationContext(sim_cfg) 195 # Set main camera 196 sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0]) 197 # Design scene 198 scene_cfg = TableTopSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0) 199 scene = InteractiveScene(scene_cfg) 200 # Play the simulator 201 sim.reset() 202 # Now we are ready! 203 print("[INFO]: Setup complete...") 204 # Run the simulator 205 run_simulator(sim, scene) 206 207 208if name == "main": 209 # run the main function 210 main() 211 # close sim app 212 simulation_app.close()

The Code Explained#

While using any task-space controller, it is important to ensure that the provided quantities are in the correct frames. When parallelizing environment instances, they are all existing in the same unique simulation world frame. However, typically, we want each environment itself to have its own local frame. This is accessible through thescene.InteractiveScene.env_origins attribute.

In our APIs, we use the following notation for frames:

The simulation world frame (denoted as w), which is the frame of the entire simulation.
The local environment frame (denoted as e), which is the frame of the local environment.
The robot’s base frame (denoted as b), which is the frame of the robot’s base link.

Since the asset instances are not “aware” of the local environment frame, they return their states in the simulation world frame. Thus, we need to convert the obtained quantities to the local environment frame. This is done by subtracting the local environment origin from the obtained quantities.

Creating an IK controller#

The DifferentialIKController class computes the desired joint positions for a robot to reach a desired end-effector pose. The included implementation performs the computation in a batched format and uses PyTorch operations. It supports different types of inverse kinematics solvers, including the damped least-squares method and the pseudo-inverse method. These solvers can be specified using theik_method argument. Additionally, the controller can handle commands as both relative and absolute poses.

In this tutorial, we will use the damped least-squares method to compute the desired joint positions. Additionally, since we want to track desired end-effector poses, we will use the absolute pose command mode.

# Create controller
diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls")
diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device)

Obtaining the robot’s joint and body indices#

The IK controller implementation is a computation-only class. Thus, it expects the user to provide the necessary information about the robot. This includes the robot’s joint positions, current end-effector pose, and the Jacobian matrix.

While the attribute assets.ArticulationData.joint_pos provides the joint positions, we only want the joint positions of the robot’s arm, and not the gripper. Similarly, while the attribute assets.Articulationdata.body_state_w provides the state of all the robot’s bodies, we only want the state of the robot’s end-effector. Thus, we need to index into these arrays to obtain the desired quantities.

For this, the articulation class provides the methods find_joints()and find_bodies(). These methods take in the names of the joints and bodies and return their corresponding indices.

While you may directly use these methods to obtain the indices, we recommend using theSceneEntityCfg class to resolve the indices. This class is used in various places in the APIs to extract certain information from a scene entity. Internally, it calls the above methods to obtain the indices. However, it also performs some additional checks to ensure that the provided names are valid. Thus, it is a safer option to use this class.

# Specify robot-specific parameters
if args_cli.robot == "franka_panda":
    robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"])
elif args_cli.robot == "ur10":
    robot_entity_cfg = SceneEntityCfg("robot", joint_names=[".*"], body_names=["ee_link"])
else:
    raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10")
# Resolving the scene entities
robot_entity_cfg.resolve(scene)
# Obtain the frame index of the end-effector
# For a fixed base robot, the frame index is one less than the body index. This is because
# the root body is not included in the returned Jacobians.
if robot.is_fixed_base:
    ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1
else:
    ee_jacobi_idx = robot_entity_cfg.body_ids[0]

Computing robot command#

The IK controller separates the operation of setting the desired command and computing the desired joint positions. This is done to allow for the user to run the IK controller at a different frequency than the robot’s control frequency.

The set_command() method takes in the desired end-effector pose as a single batched array. The pose is specified in the robot’s base frame.

        # reset controller
        diff_ik_controller.reset()
        diff_ik_controller.set_command(ik_commands)

We can then compute the desired joint positions using thecompute() method. The method takes in the current end-effector pose (in base frame), Jacobian, and current joint positions. We read the Jacobian matrix from the robot’s data, which uses its value computed from the physics engine.

        # obtain quantities from simulation
        jacobian = robot.root_physx_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids]
        ee_pose_w = robot.data.body_state_w[:, robot_entity_cfg.body_ids[0], 0:7]
        root_pose_w = robot.data.root_state_w[:, 0:7]
        joint_pos = robot.data.joint_pos[:, robot_entity_cfg.joint_ids]
        # compute frame in root frame
        ee_pos_b, ee_quat_b = subtract_frame_transforms(
            root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]
        )
        # compute the joint commands
        joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos)

The computed joint position targets can then be applied on the robot, as done in the previous tutorials.

    # apply actions
    robot.set_joint_position_target(joint_pos_des, joint_ids=robot_entity_cfg.joint_ids)
    scene.write_data_to_sim()

The Code Execution#

Now that we have gone through the code, let’s run the script and see the result:

./isaaclab.sh -p scripts/tutorials/05_controllers/run_diff_ik.py --robot franka_panda --num_envs 128

The script will start a simulation with 128 robots. The robots will be controlled using the IK controller. The current and desired end-effector poses should be displayed using frame markers. When the robot reaches the desired pose, the command should cycle through to the next pose specified in the script.

result of run_diff_ik.py

To stop the simulation, you can either close the window, or press Ctrl+C in the terminal.