Self-Improving Autonomous Underwater Manipulation

TL;DR

This paper presents AquaBot, an autonomous underwater manipulation system that combines imitation learning and self-optimization, outperforming human operators in speed across various tasks, marking progress towards self-improving underwater robots.

Contribution

Introducing AquaBot, a novel autonomous underwater manipulation system that integrates behavior cloning and self-learning to surpass human performance in real-world tasks.

Findings

AquaBot outperforms humans by 41% in manipulation speed.

The system demonstrates versatility across diverse underwater tasks.

Open-source hardware and software are provided.

Abstract

Underwater robotic manipulation faces significant challenges due to complex fluid dynamics and unstructured environments, causing most manipulation systems to rely heavily on human teleoperation. In this paper, we introduce AquaBot, a fully autonomous manipulation system that combines behavior cloning from human demonstrations with self-learning optimization to improve beyond human teleoperation performance. With extensive real-world experiments, we demonstrate AquaBot's versatility across diverse manipulation tasks, including object grasping, trash sorting, and rescue retrieval. Our real-world experiments show that AquaBot's self-optimized policy outperforms a human operator by 41% in speed. AquaBot represents a promising step towards autonomous and self-improving underwater manipulation systems. We open-source both hardware and software implementation details.