Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
Autonomous Underwater Vehicles (AUVs) traditionally rely on complex, heavily engineered pipelines for perception, path planning, and motion control. This paper explores the feasibility of an end-to-end Deep Reinforcement Learning (DRL) approach that maps raw sensor data directly to thruster commands, reducing manual engineering. We propose a hierarchical reinforcement learning (HRL) architecture splitting the problem into two Markov Decision Processes. A High-Level (HL) policy operating at 2Hz processes raw $84 \times 84$ pixel monocular camera frames, stacked $100 \times 100$ pixel forward-looking imaging sonar, and proprioceptive data to generate spatial subgoals. Simultaneously, a Low-...
| Authors | Elisei Shafer, Oren Gal |
|---|---|
| Primary category | cs.RO |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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