# Self-Improving Autonomous Underwater Manipulation

arXiv: https://arxiv.org/abs/2410.18969 · HTML: https://mlsanity.com/paper/2410.18969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2024.

## Built on by (most cited first, 3 of 3)

- [USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots](https://mlsanity.com/paper/2510.07869/) (2025)
- [Curiosity-Diffuser: Curiosity Guide Diffusion Models for Reliability](https://mlsanity.com/paper/2503.14833/) (2025)
- [UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation](https://mlsanity.com/paper/2603.27012/) (2026)

## Abstract (arXiv)

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.