mlsanityData to 14 Oct 2026

Cross-platform Learning-based Fault Tolerant Surfacing Controller for Underwater Robots

arXiv 2502.07133 ↗First posted 10 Feb 2025cs.ROcited by 0

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

Abstract (arXiv)

In this paper, we propose a novel cross-platform fault-tolerant surfacing controller for underwater robots, based on reinforcement learning (RL). Unlike conventional approaches, which require explicit identification of malfunctioning actuators, our method allows the robot to surface using only the remaining operational actuators without needing to pinpoint the failures. The proposed controller learns a robust policy capable of handling diverse failure scenarios across different actuator configurations. Moreover, we introduce a transfer learning mechanism that shares a part of the control policy across various underwater robots with different actuators, thus improving learning efficiency and generalization across platforms. To validate our approach, we conduct simulations on three different types of underwater robots: a hovering-type AUV, a torpedo shaped AUV, and a turtle-shaped robot (U-CAT). Additionally, real-world experiments are performed, successfully transferring the learned policy from simulation to a physical U-CAT in a controlled environment. Our RL-based controller demonstrates superior performance in terms of stability and success rate compared to a baseline controller, achieving an 85.7 percent success rate in real-world tests compared to 57.1 percent with a baseline controller. This research provides a scalable and efficient solution for fault-tolerant control for diverse underwater platforms, with potential applications in real-world aquatic missions.

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 9 of its 39 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

9 of 39 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  4. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
  5. 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
  6. 2017Linearly-Recurrent Autoencoder Networks for Learning Dynamicscited by 145
  7. 2020Sim2Real Transfer for Reinforcement Learning without Dynamics Randomizationcited by 47
  8. 2023Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challengescited by 18
  9. 2020Sim-to-real reinforcement learning applied to end-to-end vehicle controlcited by 5

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  3. 2023Adaptive Control Strategy for Quadruped Robots in Actuator Degradation Scenarios
  4. 2025Learning to Dock: A Simulation-based Study on Closing the Sim2Real Gap in Autonomous Underwater Docking
  5. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
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  7. 2026Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
  8. 2025Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions