Using a task-space controller

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 the scripts/tutorials/05_controllers directory.

Code for run_diff_ik.py
  1# Copyright (c) 2022-2026, The Isaac Lab Project Developers (https://github.com/isaac-sim/IsaacLab/blob/main/CONTRIBUTORS.md).
  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
 23import warp as wp
 24
 25from isaaclab.app import AppLauncher
 26
 27# add argparse arguments
 28parser = argparse.ArgumentParser(description="Tutorial on using the differential IK controller.")
 29parser.add_argument("--robot", type=str, default="franka_panda", help="Name of the robot.")
 30parser.add_argument("--num_envs", type=int, default=128, help="Number of environments to spawn.")
 31# append AppLauncher cli args
 32AppLauncher.add_app_launcher_args(parser)
 33# parse the arguments
 34args_cli = parser.parse_args()
 35
 36# launch omniverse app
 37app_launcher = AppLauncher(args_cli)
 38simulation_app = app_launcher.app
 39
 40"""Rest everything follows."""
 41
 42import torch
 43
 44import isaaclab.sim as sim_utils
 45from isaaclab.assets import AssetBaseCfg
 46from isaaclab.controllers import DifferentialIKController, DifferentialIKControllerCfg
 47from isaaclab.managers import SceneEntityCfg
 48from isaaclab.markers import VisualizationMarkers
 49from isaaclab.markers.config import FRAME_MARKER_CFG
 50from isaaclab.scene import InteractiveScene, InteractiveSceneCfg
 51from isaaclab.utils import configclass
 52from isaaclab.utils.assets import ISAAC_NUCLEUS_DIR
 53from isaaclab.utils.math import subtract_frame_transforms
 54
 55##
 56# Pre-defined configs
 57##
 58from isaaclab_assets import FRANKA_PANDA_HIGH_PD_CFG, UR10_CFG  # isort:skip
 59
 60
 61@configclass
 62class TableTopSceneCfg(InteractiveSceneCfg):
 63    """Configuration for a cart-pole scene."""
 64
 65    # ground plane
 66    ground = AssetBaseCfg(
 67        prim_path="/World/defaultGroundPlane",
 68        spawn=sim_utils.GroundPlaneCfg(),
 69        init_state=AssetBaseCfg.InitialStateCfg(pos=(0.0, 0.0, -1.05)),
 70    )
 71
 72    # lights
 73    dome_light = AssetBaseCfg(
 74        prim_path="/World/Light", spawn=sim_utils.DomeLightCfg(intensity=3000.0, color=(0.75, 0.75, 0.75))
 75    )
 76
 77    # mount
 78    table = AssetBaseCfg(
 79        prim_path="{ENV_REGEX_NS}/Table",
 80        spawn=sim_utils.UsdFileCfg(
 81            usd_path=f"{ISAAC_NUCLEUS_DIR}/Props/Mounts/Stand/stand_instanceable.usd", scale=(2.0, 2.0, 2.0)
 82        ),
 83    )
 84
 85    # articulation
 86    if args_cli.robot == "franka_panda":
 87        robot = FRANKA_PANDA_HIGH_PD_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
 88    elif args_cli.robot == "ur10":
 89        robot = UR10_CFG.replace(prim_path="{ENV_REGEX_NS}/Robot")
 90    else:
 91        raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10")
 92
 93
 94def run_simulator(sim: sim_utils.SimulationContext, scene: InteractiveScene):
 95    """Runs the simulation loop."""
 96    # Extract scene entities
 97    # note: we only do this here for readability.
 98    robot = scene["robot"]
 99
100    # Create controller
101    diff_ik_cfg = DifferentialIKControllerCfg(command_type="pose", use_relative_mode=False, ik_method="dls")
102    diff_ik_controller = DifferentialIKController(diff_ik_cfg, num_envs=scene.num_envs, device=sim.device)
103
104    # Markers
105    frame_marker_cfg = FRAME_MARKER_CFG.copy()
106    frame_marker_cfg.markers["frame"].scale = (0.1, 0.1, 0.1)
107    ee_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_current"))
108    goal_marker = VisualizationMarkers(frame_marker_cfg.replace(prim_path="/Visuals/ee_goal"))
109
110    # Define goals for the arm (x,y,z,qx,qy,qz,qw)
111    ee_goals = [
112        [0.5, 0.5, 0.7, 0, 0.707, 0, 0.707],
113        [0.5, -0.4, 0.6, 0.707, 0, 0, 0.707],
114        [0.5, 0, 0.5, 1.0, 0.0, 0.0, 0.0],
115    ]
116    ee_goals = torch.tensor(ee_goals, device=sim.device)
117    # Track the given command
118    current_goal_idx = 0
119    # Create buffers to store actions
120    ik_commands = torch.zeros(scene.num_envs, diff_ik_controller.action_dim, device=robot.device)
121    ik_commands[:] = ee_goals[current_goal_idx]
122
123    # Specify robot-specific parameters
124    if args_cli.robot == "franka_panda":
125        robot_entity_cfg = SceneEntityCfg("robot", joint_names=["panda_joint.*"], body_names=["panda_hand"])
126    elif args_cli.robot == "ur10":
127        robot_entity_cfg = SceneEntityCfg("robot", joint_names=[".*"], body_names=["ee_link"])
128    else:
129        raise ValueError(f"Robot {args_cli.robot} is not supported. Valid: franka_panda, ur10")
130    # Resolving the scene entities
131    robot_entity_cfg.resolve(scene)
132    # Obtain the frame index of the end-effector
133    # For a fixed base robot, the frame index is one less than the body index. This is because
134    # the root body is not included in the returned Jacobians.
135    if robot.is_fixed_base:
136        ee_jacobi_idx = robot_entity_cfg.body_ids[0] - 1
137    else:
138        ee_jacobi_idx = robot_entity_cfg.body_ids[0]
139
140    # Define simulation stepping
141    sim_dt = sim.get_physics_dt()
142    count = 0
143    # Simulation loop
144    while simulation_app.is_running():
145        # reset
146        if count % 150 == 0:
147            # reset time
148            count = 0
149            # reset joint state
150            joint_pos = wp.to_torch(robot.data.default_joint_pos).clone()
151            joint_vel = wp.to_torch(robot.data.default_joint_vel).clone()
152            robot.write_joint_position_to_sim_index(position=joint_pos)
153            robot.write_joint_velocity_to_sim_index(velocity=joint_vel)
154            robot.reset()
155            # reset actions
156            ik_commands[:] = ee_goals[current_goal_idx]
157            joint_pos_des = joint_pos[:, robot_entity_cfg.joint_ids].clone()
158            # reset controller
159            diff_ik_controller.reset()
160            diff_ik_controller.set_command(ik_commands)
161            # change goal
162            current_goal_idx = (current_goal_idx + 1) % len(ee_goals)
163        else:
164            # obtain quantities from simulation
165            jacobian = robot.root_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids]
166            ee_pose_w = wp.to_torch(robot.data.body_pose_w)[:, robot_entity_cfg.body_ids[0]]
167            root_pose_w = wp.to_torch(robot.data.root_pose_w)
168            joint_pos = wp.to_torch(robot.data.joint_pos)[:, robot_entity_cfg.joint_ids]
169            # compute frame in root frame
170            ee_pos_b, ee_quat_b = subtract_frame_transforms(
171                root_pose_w[:, 0:3], root_pose_w[:, 3:7], ee_pose_w[:, 0:3], ee_pose_w[:, 3:7]
172            )
173            # compute the joint commands
174            joint_pos_des = diff_ik_controller.compute(ee_pos_b, ee_quat_b, jacobian, joint_pos)
175
176        # apply actions
177        robot.set_joint_position_target_index(target=joint_pos_des, joint_ids=robot_entity_cfg.joint_ids)
178        scene.write_data_to_sim()
179        # perform step
180        sim.step()
181        # update sim-time
182        count += 1
183        # update buffers
184        scene.update(sim_dt)
185
186        # obtain quantities from simulation
187        ee_pose_w = wp.to_torch(robot.data.body_state_w)[:, robot_entity_cfg.body_ids[0], 0:7]
188        # update marker positions
189        ee_marker.visualize(ee_pose_w[:, 0:3], ee_pose_w[:, 3:7])
190        goal_marker.visualize(ik_commands[:, 0:3] + scene.env_origins, ik_commands[:, 3:7])
191
192
193def main():
194    """Main function."""
195    # Load kit helper
196    sim_cfg = sim_utils.SimulationCfg(dt=0.01, device=args_cli.device)
197    sim = sim_utils.SimulationContext(sim_cfg)
198    # Set main camera
199    sim.set_camera_view([2.5, 2.5, 2.5], [0.0, 0.0, 0.0])
200    # Design scene
201    scene_cfg = TableTopSceneCfg(num_envs=args_cli.num_envs, env_spacing=2.0)
202    scene = InteractiveScene(scene_cfg)
203    # Play the simulator
204    sim.reset()
205    # Now we are ready!
206    print("[INFO]: Setup complete...")
207    # Run the simulator
208    run_simulator(sim, scene)
209
210
211if __name__ == "__main__":
212    # run the main function
213    main()
214    # close sim app
215    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 the scene.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 the ik_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 the SceneEntityCfg 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 the compute() 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_view.get_jacobians()[:, ee_jacobi_idx, :, robot_entity_cfg.joint_ids]
            ee_pose_w = wp.to_torch(robot.data.body_pose_w)[:, robot_entity_cfg.body_ids[0]]
            root_pose_w = wp.to_torch(robot.data.root_pose_w)
            joint_pos = wp.to_torch(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_index(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.