Physics-informed Temporal Difference Metric Learning for Robot Motion Planning
This addresses motion planning for robots in complex and unseen environments, representing an incremental advance over prior self-supervised methods.
The paper tackled the problem of self-supervised motion planning in complex environments by proposing a temporal difference metric learning approach that solves the Eikonal equation more accurately, resulting in significant performance improvements over existing methods for robot configurations up to 12 degrees of freedom.
The motion planning problem involves finding a collision-free path from a robot's starting to its target configuration. Recently, self-supervised learning methods have emerged to tackle motion planning problems without requiring expensive expert demonstrations. They solve the Eikonal equation for training neural networks and lead to efficient solutions. However, these methods struggle in complex environments because they fail to maintain key properties of the Eikonal equation, such as optimal value functions and geodesic distances. To overcome these limitations, we propose a novel self-supervised temporal difference metric learning approach that solves the Eikonal equation more accurately and enhances performance in solving complex and unseen planning tasks. Our method enforces Bellman's principle of optimality over finite regions, using temporal difference learning to avoid spurious local minima while incorporating metric learning to preserve the Eikonal equation's essential geodesic properties. We demonstrate that our approach significantly outperforms existing self-supervised learning methods in handling complex environments and generalizing to unseen environments, with robot configurations ranging from 2 to 12 degrees of freedom (DOF).