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name: retargeting description: "Retargets frame and tool-pose sequences to cuRobo robot joint trajectories using global IK or local MPC with collision constraints." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Motion retargeting

Use this route for humanoid/high-DoF retargeting, frame-by-frame tool goals, sequence goals, joint locks, global IK, local MPC, and scene-aware retargeting. Read api-reference.md and retargeting-workflows.md.

Core workflow

1. Choose a validated robot config and define a ToolPoseCriteria dictionary for each tool/frame to track. Verify frame names, weights, tolerances, and position/orientation conventions.
2. Build MotionRetargeterCfg.create(robot=..., tool_pose_criteria=...) with num_envs, use_mpc, seed counts, steps_per_target, timing, collision, and optional scene settings.
3. Feed one frame to solve_frame or a correctly shaped sequence to solve_sequence; inspect success, joint limits, collision metrics, and temporal smoothness before commanding.
4. Lock joints only when the mechanical/task constraint is explicit. For high-DoF robots, start with a short sequence and a small environment count, then scale GPU memory and control points.
5. Keep external SOMA/input datasets and interactive playback separate from the reusable solver path. Use the bounded scripts/retarget_config_smoke.py helper for criteria parsing and troubleshooting.md when shapes or criteria fail.

For the underlying IK/MPC contracts, read ik and mpc-optimization.