mlsanityData to 14 Oct 2026

Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles

arXiv 2608.08598 ↗First posted 9 Aug 2026cs.ROcited by 0

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

Abstract (arXiv)

With the continuous improvement of computational capabilities, end-to-end reinforcement learning has been rapidly developed for remotely operated vehicles control. Nevertheless, existing end-to-end reinforcement-learningbased methods still face challenges in achieving optimal control under oceancurrent disturbances. In particular, there remains a lack of a unified control framework that can simultaneously achieve low steady-state tracking error, rapid transient response, energy-efficient operation, and smooth controlforce outputs under disturbances. To address the issue, this paper proposes the thrust smoothness rapid current adaptation proximal policy optimization (TSRCA-PPO) method which learns a near-optimal strategy by a twostage distillation learning framework. The core innovations of this work lie in the reward-function design and the privileged multi-encoder architecture. Ablation studies validate the effectiveness of each module. Simulation results demonstrate that the proposed TSRCA-PPO method consistently outperforms the conventional cascaded P-PID controller across all evaluation metrics. Specifically, TSRCA-PPO reduces the steady-state position error, steady-state attitude error, settling time, energy index, and thrustsmoothness index to 42.7%, 76.5%, 10.6%, 93.5%, and 15.9% of the corresponding P-PID values, respectively.

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 11 of its 28 references that are in the map. First posted on arXiv 9 Aug 2026.

Builds on

11 of 28 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  4. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  5. 2019Learning by Cheatingcited by 383
  6. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  7. 2020Regularizing Action Policies for Smooth Control with Reinforcement Learningcited by 59
  8. 2024Gradient-based Regularization for Action Smoothness in Robotic Control with Reinforcement Learningcited by 10
  9. 2024PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robotscited by 8
  10. 2017Online Approximate Optimal Station Keeping of a Marine Craft in the Presence of a Currentcited by 3
  11. 2025Fast Policy Learning for 6-DOF Position Control of Underwater Vehiclescited by 3

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  3. 2025Toward 6-DOF Autonomous Underwater Vehicle Energy-Aware Position Control based on Deep Reinforcement Learning: Preliminary Results
  4. 2026A Unified Dynamics Framework for Reinforcement Learning and Classical Control of a Six-DOF Pipeline-Tracking ROV in NVIDIA Isaac Sim
  5. 2026Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning
  6. 2025When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks
  7. 2026Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry
  8. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles