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 challenging 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 planner that balances progress toward the goal with the cost of reaching nearby poses.
Results
We evaluate the framework across 2D and 3D rigid-body planning tasks and non-prehensile manipulation through object pushing in diverse environments.
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.
Ablation Studies
Ablation studies further validate the contributions of both the learned representation and the proposed planning strategy.
Experiments
Finally, closed-loop pushing experiments under execution deviations and external disturbances demonstrate that fast replanning enables reactive and adaptive task execution.