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

2025-11-06

AI Summary

Sample-based motion planners (SBMPs) suffer from low efficiency and slow convergence in complex environments due to uniform sampling, which wastes computational resources on low-value regions. Method: This paper proposes a theoretically grounded non-uniform sampling strategy that integrates conformal prediction—introduced to motion planning for the first time—to provide distribution-free, confidence-level–controlled guarantees that the optimal solution lies within certified high-probability sampling regions. Leveraging heuristic path predictors (e.g., A* or vision-language models), the method generates an initial trajectory and quantifies its epistemic uncertainty to identify high-value sampling zones. Contribution/Results: The approach significantly accelerates feasible path discovery while ensuring theoretical validity of sampling coverage. Extensive experiments demonstrate superior generalization and robustness over state-of-the-art baselines, particularly in previously unseen environments, validating both computational efficiency and reliability under uncertainty.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Develops non-uniform sampling to accelerate motion planning efficiency
• Guarantees probabilistic correctness of sampling regions using conformal prediction
• Improves path finding speed and generalization in complex environments

Innovation

• Non-uniform sampling biased toward certified regions
• Conformal prediction quantifies predictor uncertainty
• Probabilistically guaranteed optimal solution in sampling sets