微分可能CVaRバリア関数によるリスク適応強化学習
Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
不確実な障害物の動きがある混雑環境でのナビゲーションのため、強化学習とCVaRバリア関数に基づく安全層を組み合わせ、リスクレベルと安全マージンを適応的に学習する枠組みを提案した。
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著者: Xinyi Wang, Taekyung Kim, Bardh Hoxha, Georgios Fainekos, Dimitra Panagou
分類: cs.RO
原文アブストラクト
Planning through crowded environments under uncertain obstacle motions remains difficult, as stochastic interactions often induce overly conservative behavior or reduced efficiency. To address this challenge, we propose an end-to-end risk adaptation framework for crowd navigation under obstacle-motion uncertainty modeled by a Gaussian mixture model. The framework combines reinforcement learning~(RL) with a differentiable quadratic-program safety layer based on Conditional Value-at-Risk~(CVaR) barrier functions, jointly learning nominal control input, risk level, and safety margin and enforcing explicit probabilistic safety constraints. This design enables context-aware adaptation, promoting efficient behavior while invoking caution only when necessary. We conduct extensive evaluations in dynamic, uncertain, and crowded environments across varying obstacle densities and robot models, and further assess generalization under three out-of-distribution cases. Comparisons across optimization-based, RL-based, and integrated RL and optimization methods are provided, and the proposed method is shown to deliver the strongest overall performance in safety, efficiency, and generalization under uncertainty.
