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效率与协作 · SKILL.md

motion-planning

motion-planning

【效率与协作】Plans collision-aware c-space, pose, and grasp motions with cuRobo's composed IK, graph-planning, and trajectory-optimization APIs.

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GitHub

name: motion-planning description: "Plans collision-aware c-space, pose, and grasp motions with cuRobo's composed IK, graph-planning, and trajectory-optimization APIs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Motion planning

Use this route for collision-free robot paths, pose/c-space/grasp planning, batched planning, graph planner selection, or planner scene updates. Read workflows.md for composition and api-reference.md for result handling.

Core workflow

  1. Build MotionPlannerCfg.create with a robot, IK/trajopt/graph/metrics YAMLs, scene model, collision cache, batch shape, and CUDA DeviceCfg.
  2. Construct MotionPlanner, inspect its tool frames/joint names, and prepare a named start JointState plus a reachable pose, c-space, or grasp goal.
  3. Call the matching plan_pose, plan_cspace, or plan_grasp; inspect success, validity, collision metrics, and the interpolated trajectory before sending any command.
  4. For a workcell update, call update_world; use attachment-manager and link-collision toggles only with an explicit scene model.
  5. Scale seeds, graph samples, horizon, and batch only after a tiny plan works. Keep CUDA graphs enabled in production and use scripts/plan_smoke.py for a non-interactive check.

IK is an internal planning stage; use ik for target solving alone and collision-scenes for obstacle schema.

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