Researchers Develop Deep Reinforcement Learning System for Autonomous Underwater Vehicle Navigation
Researchers have proposed a hierarchical deep reinforcement learning (DRL) architecture that maps raw sensor data directly to thruster commands for autonomous underwater vehicle (AUV) navigation, bypassing traditional engineered pipelines. The system splits control into a high-level policy generating spatial subgoals at 2Hz and a low-level policy issuing thruster commands at 10Hz, trained and evaluated in the HoloOcean simulator. The approach achieves trajectory lengths within 4–6% of an RRT* planning baseline and shows robustness to sensor noise, though it struggles to generalize to novel obstacle shapes.
A preprint submitted to arXiv presents an end-to-end deep reinforcement learning framework for autonomous underwater vehicle (AUV) navigation that eliminates the need for separately engineered perception, path planning, and motion control modules. The hierarchical reinforcement learning (HRL) architecture divides the problem into two Markov Decision Processes: a high-level policy operating at 2Hz that processes monocular camera frames, forward-looking imaging sonar, and proprioceptive data to produce spatial subgoals, and a low-level policy at 10Hz that translates those subgoals into direct thruster commands. The high-level policy is trained using Reinforcement Learning from Prior Demonstrations (RLPD) within a modified Sample-Efficient Robotic Reinforcement Learning (SERL) framework, while the low-level policy employs Soft Actor-Critic (SAC) combined with Hindsight Experience Replay (HER). Evaluated in the high-fidelity HoloOcean simulator, the system achieves trajectory lengths within 4–6% of an RRT* optimal planning baseline and demonstrates robustness to simulated sensor noise and reduced visibility conditions. However, the authors acknowledge a key limitation: the policy generalizes poorly to previously unseen areas containing novel obstacle geometries, indicating that further work is needed before real-world deployment.
What's missing
The study is conducted entirely in simulation (HoloOcean) with no real-world hardware validation, leaving open questions about the sim-to-real transfer gap, hydrodynamic modeling accuracy, and performance under actual underwater conditions such as currents, biofouling, and communication constraints. The authors do not report training compute costs or wall-clock training times, which are relevant for assessing practical scalability. Generalization to novel obstacle shapes remains an unresolved limitation explicitly noted by the authors.
What different sources said
- arXiv cs.LGCenter
Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
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