未知外乱下の安全なナビゲーションのための信念誘導型残差適応制御
BRACE: Belief-guided Residual Adaptive Control for Safe Navigation under Unknown Disturbances
モデル予測誤差から外乱の信念を推定し、高レベル方策で参照軌道を適応させる階層型強化学習フレームワークBRACEを提案。急変する風場でのクアッドコプター実験でMPCやRLベースラインを上回る到達回避性能を示した。
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著者: Jaeyoun Choi, Niklas Schmid, Oswin So, Augustinos Saravanos, Chuchu Fan
分類: cs.RO, eess.SY
原文アブストラクト
Disturbances encountered during autonomous navigation generally vary across space, often discontinuously, as obstacles redirect wind and changing terrain alters traction. Tracking controllers typically assume sufficient control authority to mitigate disturbances and maintain safety; the regime where this assumption fails remains underexplored. We present Belief-guided Residual Adaptive Control (BRACE), a hierarchical reinforcement learning (RL) framework for reach-avoid navigation under unknown, bounded, spatially varying disturbances. BRACE infers a disturbance belief from model prediction errors and uses that to condition a high-level policy that adapts the reference trajectory. The policy combines a nominal and a belief-guided residual term, trained jointly with a likelihood-ratio advantage reweighting heuristic. We evaluate BRACE in three simulated quadcopter environments with abruptly switching wind fields, and validate it on Crazyflie hardware with fan-generated disturbances. Across these settings, BRACE improves reach-avoid performance over model predictive control and RL baselines.