mlsanityData to 14 Oct 2026

Swimming Under Constraints: A Safe Reinforcement Learning Framework for Quadrupedal Bio-Inspired Propulsion

arXiv 2603.04073 ↗First posted 4 Mar 2026cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 31 references that are in the map. First posted on arXiv 4 Mar 2026.

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12 of 31 references.

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  1. 2025Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning5 Jun

Other references · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2025DAPO: An Open-Source LLM Reinforcement Learning System at Scalecited by 2,810
  3. 2015Trust Region Policy Optimizationcited by 2,580
  4. 2017Constrained Policy Optimizationcited by 983
  5. 2018Reward Constrained Policy Optimizationcited by 417
  6. 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
  7. 2020Learning Agile Robotic Locomotion Skills by Imitating Animalscited by 251
  8. 2020Projection-Based Constrained Policy Optimizationcited by 203
  9. 2025Geometric-Mean Policy Optimizationcited by 111
  10. 2022CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learningcited by 20
  11. 2025Learn to Swim: Data-Driven LSTM Hydrodynamic Model for Quadruped Robot Gait Optimizationcited by 1

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  1. 2024Learning Agile Swimming: An End-to-End Approach without CPGs
  2. 2026Egocentric Station Holding of Robotic Fish in Unknown Turbulent Background Flow ↗
  3. 2025Navigation of a Three-Link Microswimmer via Deep Reinforcement Learning
  4. 2026Extending the Speed Limit of Quadrupedal Locomotion via Refined Actuator Modeling and Adaptive Command Scheduling ↗
  5. 2024Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
  6. 2025A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows
  7. 2025Learning to Walk With Less: A Dyna-Style Approach to Quadrupedal Locomotion
  8. 2024Fine Tuning Swimming Locomotion Learned from Mosquito Larvae