拘束下での遊泳：四足生物模倣推進のための安全強化学習フレームワーク
Swimming Under Constraints: A Safe Reinforcement Learning Framework for Quadrupedal Bio-Inspired Propulsion
四足ロボットの遊泳を、推力を最大化しつつ不安定化力を最小化する制約付き最適化問題として定式化し、PID調整ラグランジュ乗数を用いた安全強化学習手法ACPPO-PIDを提案。模倣学習と実機曳航試験で初期化・調整し、自由遊泳への転移を実証した。
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著者: Xinyu Cui, Fei Han, Hang Xu, Yongcheng Zeng, Luoyang Sun, Ruizhi Zhang, Jian Zhao, Haifeng Zhang, Weikun Li, Hao Chen, Jun Wang, Dixia Fan
分類: cs.RO
原文アブストラクト
Bio-inspired aquatic propulsion offers high thrust and maneuverability but is prone to destabilizing forces such as lift fluctuations, which are further amplified by six-degree-of-freedom (6-DoF) fluid coupling. We formulate quadrupedal swimming as a constrained optimization problem that maximizes forward thrust while minimizing destabilizing fluctuations. Our proposed framework, Accelerated Constrained Proximal Policy Optimization with a PID-regulated Lagrange multiplier (ACPPO-PID), enforces constraints with a PID-regulated Lagrange multiplier, accelerates learning via conditional asymmetric clipping, and stabilizes updates through cycle-wise geometric aggregation. Initialized with imitation learning and refined through on-hardware towing-tank experiments, ACPPO-PID produces control policies that transfer effectively to quadrupedal free-swimming trials. Results demonstrate improved thrust efficiency, reduced destabilizing forces, and faster convergence compared with state-of-the-art baselines, underscoring the importance of constraint-aware safe RL for robust and generalizable bio-inspired locomotion in complex fluid environments.
関連論文
- 関節型遊泳ロボットのための誘導付き深層強化学習強化学習/遊泳ロボット
