受约束的游泳：一种安全强化学习框架用于四足生物启发推进
Swimming Under Constraints: A Safe Reinforcement Learning Framework for Quadrupedal Bio-Inspired Propulsion
AI总结：
本文提出了一种安全强化学习框架ACPPO-PID，通过约束优化提升四足生物启发推进的推力效率和稳定性。
摘要：
生物启发的水下推进提供了高推力和机动性，但容易受到如升力波动等不稳定力的影响，这种影响在六自由度（6-DoF）流体耦合下进一步加剧。我们将四足游泳 formulation 为一个约束优化问题，旨在最大化前进推力同时最小化不稳定波动。我们提出的框架，加速约束近端策略优化加PID调节拉格朗日乘数（ACPPO-PID），通过PID调节的拉格朗日乘数强制约束，通过条件不对称裁剪加速学习，并通过周期性几何聚合稳定更新。初始化于模仿学习并通过硬件拖曳池实验进行优化，ACPPO-PID产生能够有效转移到四足自由游泳试验的控制策略。结果表明，与最先进的基线相比，推力效率提高，不稳定力减少，收敛速度加快，凸显了约束意识安全强化学习在复杂流体环境中稳健且可推广的生物启发运动中的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
