Conformalized Non-uniform Sampling Strategies for Accelerated Sampling-based Motion Planning

TL;DR

This paper presents a novel non-uniform sampling strategy for sampling-based motion planners that biases sampling toward certified regions with probabilistic guarantees, improving efficiency and generalization in complex environments.

Contribution

It introduces the first non-uniform sampling method for SBMPs that uses conformal prediction to create probabilistically certified regions, enhancing planning speed and robustness.

Findings

Faster feasible path discovery in complex environments.

Better generalization to unseen environments.

Provides probabilistic guarantees on sampling regions.

Abstract

Sampling-based motion planners (SBMPs) are widely used to compute dynamically feasible robot paths. However, their reliance on uniform sampling often leads to poor efficiency and slow planning in complex environments. We introduce a novel non-uniform sampling strategy that integrates into existing SBMPs by biasing sampling toward `certified' regions. These regions are constructed by (i) generating an initial, possibly infeasible, path using any heuristic path predictor (e.g., A* or vision-language models) and (ii) applying conformal prediction to quantify the predictor's uncertainty. This process yields prediction sets around the initial-guess path that are guaranteed, with user-specified probability, to contain the optimal solution. To our knowledge, this is the first non-uniform sampling approach for SBMPs that provides such probabilistically correct guarantees on the sampling regions. Extensive evaluations demonstrate that our method consistently finds feasible paths faster and generalizes better to unseen environments than existing baselines.