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论文arXiv 2608.085982 个月前

面向遥控潜水器平滑6自由度推力控制与快速适应海流的知识蒸馏端到端强化学习 ​

针对ROV在海流扰动下难兼顾精度、响应、能耗和平滑推力，提出TSRCA-PPO并在仿真中验证优于P-PID。

AI 整理
  • 本体 SDK 与控制
  • 仿真与训练
  • 具身模型

原文链接：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 · 发布日期：2026-08-09）

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

针对ROV在海流扰动下难兼顾精度、响应、能耗和平滑推力，提出TSRCA-PPO并在仿真中验证优于P-PID。

核心要点 ​

  • 现有端到端强化学习控制在海流扰动下仍难实现综合最优控制。
  • TSRCA-PPO通过两阶段蒸馏学习框架学习近最优控制策略。
  • 核心设计包括奖励函数与特权多编码器架构，并经消融验证有效性。
  • 仿真中各评估指标持续优于级联P-PID，误差、调节时间、能耗与推力平滑性指标均降低。

验证范围与局限：验证范围为仿真实验，并与传统级联P-PID控制器比较。

英文摘要 (Abstract) ​

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.

作者：Tiankuang Wen, Huiping Li, Gang Liu, Yong Jiang

来源

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  1. arXiv 2608.08598：Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles访问 2026-10-21