Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks

TL;DR

This paper introduces an environment-aware reinforcement learning framework for autonomous underwater vehicles that dynamically incorporates environmental data and vehicle characteristics, leading to improved adaptability and decision-making in underwater tasks.

Contribution

It presents a novel RL framework integrating environment-aware networks and LLM-based refinement, enabling AUVs to adapt to complex underwater environments more effectively.

Findings

Enhanced AUV performance in diverse underwater conditions

Improved real-time environmental adaptation capabilities

Robustness demonstrated through comprehensive experiments

Abstract

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV's situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework's superior performance, robustness and adaptability.