Manipulation IK

Patterns reference Isaac Sim docs and local example files; embedded code is a pattern sketch, not the canonical source. Always read the linked example; upstream code reflects the installed Isaac Sim version.

When to use

• Control an articulated arm to reach, grasp, transport, place.
• Set up IK-based end-effector control (vs joint-space).
• Store reusable robot poses as named poses and apply them later.
• Set up grasping (FixedJoint, SurfaceGripper, contact-based).
• Validate manipulation success with a feedback loop.

Pick the right IK stack

Local example files (canonical source)

Relative to $ISAAC_SIM_DIR/source/standalone_examples/api/isaacsim.robot.experimental.manipulators/:

Robot-side implementations: source/extensions/isaacsim.robot.experimental.manipulators.examples/isaacsim/robot/experimental/manipulators/examples/{franka,universal_robots}/*.py (e.g. UR10, Franka classes — differential_inverse_kinematics, set_end_effector_pose, reset_to_default_pose).

Legacy/deprecated examples under $ISAAC_SIM_DIR/source/standalone_examples/deprecated/api/isaacsim.robot.manipulators/.

Migration: for the omni.isaac.franka / omni.isaac.universal_robots / omni.isaac.manipulators → isaacsim.robot.manipulators* rename map (including the experimental examples extension), see Renaming Extensions.

Docs references

Differential IK pattern (modern Articulation)

Pattern source: UR10.differential_inverse_kinematics (UR wrapper) + UR10FollowTarget.move_to_target. The wrapper does:

1. Get the Jacobian: self.get_jacobian_matrices() (shape includes a virtual base for fixed-base robots; always slice past the base DOFs).
2. Compute the 6-DOF pose error from current EE pose to goal.
3. Apply the chosen solver to map error -> joint delta:
   • damped-least-squares: dq = J^T (J J^T + lambda^2 I)^-1 . error (default).
   • pseudoinverse, transpose, singular-value-decomposition also available.
4. Push as set_dof_position_targets(current + dq, dof_indices=arm_dofs).

# Conceptual sketch — see ur10.py and follow_target_with_ik.py for the live code
from isaacsim.core.experimental.prims import Articulation, RigidPrim

arm = MyArm("/World/robot", create_robot=True)
ee  = arm.end_effector_link
J   = arm.get_jacobian_matrices().numpy()[:, arm.end_effector_link_index - 1, :, :arm_dofs]
cur_pos, cur_q = ee.get_world_poses()
dq = arm.differential_inverse_kinematics(
    jacobian_end_effector=J,
    current_position=cur_pos.numpy(),
    current_orientation=cur_q.numpy(),
    goal_position=target_pos,
    goal_orientation=target_quat,
    method="damped-least-squares",
    method_cfg={"scale": 1.0, "damping": 0.05, "min_singular_value": 1e-5},
)
arm.set_dof_position_targets(cur_dofs[:, :arm_dofs] + dq, dof_indices=list(range(arm_dofs)))

Tuning (start conservative, increase after stability):

Aggressive settings cause PhysX divergence under payload.

Hybrid IK + joint-space (arms with < 6 DOF)

Pure differential IK on under-actuated arms fails on:

• Large lateral transport with payload.
• Configurations near kinematic singularities.
• Sweeping through joint limits.

Pattern: IK for precision (approach, descent, final placement), joint-space interpolation for long transport (lift, traverse, descend). See franka/pick_place.py for the sequenced version Isaac Sim ships.

Schema-native IK + Named Poses (RobotPoser)

For pose authoring, persistence, and replay use isaacsim.robot.poser. It wraps a kinematic chain (from usd.schema.isaac.robot_schema) and provides IK plus a named-pose library stored as IsaacNamedPose prims on the robot.

from isaacsim.robot.poser import (
    RobotPoser, Transform,
    store_named_pose, apply_pose_by_name,
    list_named_poses, delete_named_pose,
    export_poses, import_poses,
    validate_robot_schema,
)

# Robot must carry IsaacRobotAPI (applied during URDF/MJCF import).
validate_robot_schema(stage, robot_prim)

poser = RobotPoser(stage, robot_prim, start_prim, end_prim)
target = Transform(position=[0.5, 0.2, 0.8], orientation=[1, 0, 0, 0])
result = poser.solve_ik(target)
if result.success:
    poser.apply_pose(result.joints)
    store_named_pose(stage, robot_prim, "pick_position", result)

# Later:
apply_pose_by_name(stage, robot_prim, "pick_position")
export_poses(stage, robot_prim, "/path/to/poses.json")

Standalone helpers (no RobotPoser needed) for FK / DOF target application:

from isaacsim.robot.poser import apply_joint_state, apply_joint_state_anchored
apply_joint_state(stage, robot_prim, joint_values)          # FK off-sim / DOF targets when playing
apply_joint_state_anchored(stage, robot_prim, joint_values, # keep anchor at world pose
                           anchor_prim=base_link_prim)

The IK solver is pluggable via usd.schema.isaac.robot_schema.IKSolverRegistry; the bundled LM solver (robot_schema.lm_ik) is the default.

Obstacle-aware motion (cuMotion / PINK)

Use the documented loaders for both stacks; do not hand-construct configs. Read the full tutorials before extending:

• cuMotion RMP flow tutorial -> docs/isaacsim/cumotion/tutorial_rmpflow.rst
• cuMotion world interface
• cuMotion robot configuration
• PINK IK controller tutorial -> docs/isaacsim/pink/tutorial_ik_controller.rst
• PINK robot configuration

cuMotion (RMP flow)

from isaacsim.robot_motion.cumotion import (
    RmpFlowController, CumotionWorldInterface, load_cumotion_supported_robot,
)
from isaacsim.robot_motion.motion_generation import RobotState

robot = load_cumotion_supported_robot("franka")           # config + chain
world = CumotionWorldInterface(...)                        # populate obstacles
ctrl  = RmpFlowController(
    name="rmp_franka",
    robot=robot,
    world_interface=world,
)

# Required: reset() before the first forward() each episode.
ctrl.reset(RobotState(...))                                # estimated current state
action = ctrl.forward(
    estimated_state=RobotState(...),
    setpoint_state=RobotState(...),                        # target EE pose
)

PINK (Pinocchio-based IK)

from isaacsim.robot_motion.pink import PinkIKController, load_pink_supported_robot

pink_robot = load_pink_supported_robot("franka")           # PinkRobot wrapper
controller = PinkIKController(
    name="pink_franka",
    robot=pink_robot,
    tasks=[...],                                            # frame/joint/posture tasks
)

Use load_pink_robot(...) when authoring a non-bundled robot. See the pink/tutorial_robot_configuration.rst walkthrough.

Grasps

isaacsim.replicator.grasping (GraspingManager, GraspPhase) drives grasp-dataset workflows and grasp-pose generation; see source/standalone_examples/api/isaacsim.replicator.grasping/grasping_workflow_sdg.py.

Grasp frame discovery (do this first)

Most assets do not ship with a grasp frame. Before any IK:

1. Inspect the gripper USD; find the frame at the closed-finger center.
2. If absent, add a child Xform of the gripper link positioned at the grasp center; mark it with IsaacSiteAPI (ApplySiteAPI from robot_schema) so downstream tools recognize it.
3. Use that site as the IK target. The goal pose is where the object center sits when grasped, not where the gripper body is.

Grasping

FixedJoint (simple, reliable for rigid grasps)

Pattern source: franka/pick_place.py plus UsdPhysics.FixedJoint. Always compute the gripper -> object relative transform at the moment of contact; never hardcode the offset. Hardcoded offsets + high stiffness produce PhysX snap and explosion.

SurfaceGripper (vacuum / magnetic, used by UR10 example)

Pattern source: UR10 (open_gripper, close_gripper).

from isaacsim.robot.surface_gripper import _surface_gripper as surface_gripper

iface = surface_gripper.acquire_surface_gripper_interface()
gripper_path = f"{end_effector_path}/SurfaceGripper"
iface.close_gripper(gripper_path)   # attach
iface.open_gripper(gripper_path)    # release
status = iface.get_gripper_status(gripper_path)  # GripperStatus.{Open,Closed}

Authored on the robot via usd.schema.isaac.robot_schema.CreateSurfaceGripper.

Grasp dataset workflow

For generating grasp datasets, see isaacsim.replicator.grasping (GraspingManager, GraspPhase) and source/standalone_examples/api/isaacsim.replicator.grasping/grasping_workflow_sdg.py.

Grasp validation (feedback loop)

After executing a pick-and-place, validate at three checkpoints:

Smooth motion is necessary but not sufficient. Do not declare success unless all three pass.

Rules

1. Read the local example first; this skill describes patterns, not syntax.
2. Always create or identify a grasp frame (IsaacSiteAPI) before IK.
3. Start conservative with IK gains; increase only after confirming stability.
4. The URDF importer applies PhysxArticulationAPI automatically; if you author articulations manually, apply it on the base link.
5. Run standalone scripts with $ISAAC_SIM_DIR/python.sh, not isaaclab.sh -p, when using SimulationApp directly.
6. Jacobian column layout: [virtual base DOFs | real DOFs] for fixed-base robots. Always slice past the virtual base.
7. Store reusable poses with store_named_pose; do not re-solve IK from scratch every session.
8. print() is unreliable in headless mode; use file writes for debug logging.
9. Validate visually at every phase. Smooth motion is not successful manipulation.
10. Hybrid IK + joint-space is the pragmatic default for arms with < 6 DOF.

Lessons (2026-04-08)

• SO-101 5-DOF: pure DLS IK converges for local moves (~0.008 m error) but diverges on lateral transport under load. Hybrid is required.
• FixedJoint with hardcoded offset + high stiffness causes PhysX snap and explosion. Compute the offset at grasp time.
• Jacobian virtual-base offset: easy to miss; breaks IK silently. Always slice past the base.