Train-Once Plan-Anywhere: Kinodynamic Motion Planning via Diffusion Trees

Kinodynamic motion planning is concerned with computing collision-free trajectories while abiding by a robot’s dynamic constraints. This critical problem is often tackled using sampling-based planners (SBPs) that explore the robot’s high-dimensional state space by constructing a search tree via action propagations. Although SBPs can offer global guarantees on completeness and solution quality, their performance is often hindered by slow exploration due to uninformed action sampling.  Learning-based approaches can yield significantly faster runtimes,  yet they fail to generalize to out-of-distribution (OOD) scenarios and lack critical guarantees, e.g., safety, thus limiting their deployment on physical robots. We present Diffusion Tree (DiTree): a provably-generalizable framework leveraging diffusion policies (DPs) as informed samplers to efficiently guide state-space search within SBPs. DiTree  combines DP’s ability to model complex distributions of expert trajectories, conditioned on local observations, with the completeness of SBPs to yield provably-safe solutions within a few action propagation iterations for complex dynamical systems. We demonstrate DiTree’s power with an implementation combining the popular RRT planner with a DP action sampler trained on a single environment. In comprehensive evaluations on OOD scenarios, DiTree has comparable runtimes to a standalone DP (4x faster than classical SBPs), while improving the success rate over DP and SBPs (on average).

PhD. student under the supervision of Prof. Kiril Solovey.