Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning

Key points

• Rigid-body motion planning is fundamental to robotic transport and manipulation, where both object position and orientation determine whether a body can move through cluttered environments.1
• The learned field captures local motion difficulty in translation and rotation and is paired with a sampling-based planner that balances progress toward the goal with the cost of reaching nearby poses.1
• We evaluate the framework across 2D and 3D rigid- body planning tasks and non-prehensile manipulation through object pushing in diverse environments.1
• Results show substantial improvements in planning success and inference efficiency over physics-informed neural planning baselines, while remaining competitive with classical sampling-based planners at significantly lower planning times.1

Sources (1)

• [1]Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 12:05 AM

  We propose Shape- Aware Neural Time Fields, a physics-informed neural motion planning framework that learns a reusable travel-time, or cost- to-go, representation for rigid-body motion while explicitly accounting for these distinct geometric restrictions.

  Rigid-body motion planning is fundamental to robotic transport and manipulation, where both object position and orientation determine whether a body can move through cluttered environments.

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