Enhancing Classical Motion Planners Using RL with Safety Guarantees

Abstract

Classical algorithms for autonomous navigation, while well-understood and safe, require manual parameter tuning by experts to perform well. APPL and similar methods use machine learning to dynamically adjust planner parameters during deployment. This approach maintains the safety of classical systems but remains constrained by the under lying algorithm. Instead of parameter tuning, we suggest using classical planners to regulate action selection of a reinforcement learning (RL) algorithm. The resulting policy is provably similar to the well-understood classical algorithm, performs better than both a well-tuned classical planner and an unregularized RL based policy, and can be shown to respect a user-controlled trust region even during training. In experiments, our method reduces traversal time by 8% (vs. DWA) and 43% (vs. TEB), and lowers proximity risk by 24% and 17%, respectively, while matching or surpassing learning-based baselines and aligning more closely with user preferences.