Shape-Aware Neural Time Fields for Rigid-Body Motion and Manipulation Planning
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. This coupling makes planning chal- lenging because translation and rotation can be constrained very differently by surrounding geometry. 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. The learned field captures local motion difficulty in translation and rotation and is paired with a sampling-based...
| Authors | Yanming Zhou, Yuchen Liu, Ahmed H. Qureshi |
|---|---|
| Primary category | cs.RO |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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