NeRP: Neural Rearrangement Planning for Unknown Objects
This addresses the challenge of enabling robots to manipulate diverse objects in human environments, representing an incremental advance in AI capabilities for rearrangement tasks.
The paper tackles the problem of multi-step object rearrangement planning for robots with unseen objects, proposing NeRP, a deep learning approach trained on simulation data that generalizes to real-world tasks, achieving fewer steps and less planning time compared to baselines.
Robots will be expected to manipulate a wide variety of objects in complex and arbitrary ways as they become more widely used in human environments. As such, the rearrangement of objects has been noted to be an important benchmark for AI capabilities in recent years. We propose NeRP (Neural Rearrangement Planning), a deep learning based approach for multi-step neural object rearrangement planning which works with never-before-seen objects, that is trained on simulation data, and generalizes to the real world. We compare NeRP to several naive and model-based baselines, demonstrating that our approach is measurably better and can efficiently arrange unseen objects in fewer steps and with less planning time. Finally, we demonstrate it on several challenging rearrangement problems in the real world.