DODGER: 動的障害物環境におけるロボットナビゲーションのための安全誘導強化学習
DODGER: Safety-Guided Reinforcement Learning for Robot Navigation Among Dynamic Obstacles
CBFによる安全フィルタを訓練時の参照として活用し、ポリシー探索を制限せずに動的障害物を回避する強化学習フレームワークを提案。ヒューマノイドのシミュレーションと実機実験で検証した。
著者: Sanghyuk Park, Kwanwoo Lee, Taekyung Kim, Seohyeon Lim, Yisoo Lee
分類: cs.RO, eess.SY
原文アブストラクト
Robots operating in human-centered environments must safely navigate among multiple dynamic obstacles to avoid collisions with people and surrounding infrastructure. Control barrier functions (CBFs) provide an effective mechanism for safety filtering, and recent CBF-based reinforcement learning (RL) methods embed such safety information into learned policies. However, executing only safety-filtered actions during training can restrict policy exploration, a limitation that becomes particularly consequential in dynamic scenes where safety depends on relative robot-obstacle motion. We propose DODGER, a safety-guided RL framework that directly executes policy-generated actions to drive training rollouts while using CBF-filtered references and constraint violations to shape the policy toward collision-avoidance behavior. We evaluate DODGER through a Dubins-car safety analysis and demonstrate goal-directed navigation among multiple dynamic obstacles in full-order humanoid simulation and real-world humanoid experiments using LiDAR-based perception, without a runtime safety filter.