让四足机器人安全游泳，抑制不稳定力并提升推进效率。
Swimming Under Constraints: A Safe Reinforcement Learning Framework for Quadrupedal Bio-Inspired Propulsion
- 用带PID调节的拉格朗日乘子约束不稳定的流体力。
- 在真实水池实验中实现更快收敛与更高推进效率。
- 适合研究仿生机器人在复杂流体中自主控制的学者。
仿生水下推进具有高推力和高机动性，但易受升力波动等失稳力影响，且六自由度流体耦合会加剧该问题。本文将四足游泳建模为约束优化问题，旨在最大化前向推力同时最小化失稳波动。提出ACPPO-PID框架：通过PID调节的拉格朗日乘子施加约束，利用条件非对称裁剪加速学习，通过周期内几何聚合稳定更新。模型以模仿学习初始化，并经实地拖曳水池实验优化，成功迁移到自由游泳测试。结果表明，相比最先进基线，该方法显著提升推力效率、降低失稳力并加快收敛，证明了约束感知安全强化学习在复杂流体环境中实现鲁棒、可泛化的仿生运动的重要性。
原文摘要 · Abstract (English)
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.
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