# Learning of Multi-Context Models for Autonomous Underwater Vehicles

arXiv: https://arxiv.org/abs/1809.06179 · HTML: https://mlsanity.com/paper/1809.06179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2018.

## Built on by (most cited first, 2 of 2)

- [Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring](https://mlsanity.com/paper/2604.12645/) (2026)
- [Enhancing AI System Resiliency: Formulation and Guarantee for LSTM Resilience Based on Control Theory](https://mlsanity.com/paper/2505.17696/) (2025)

## Abstract (arXiv)

Multi-context model learning is crucial for marine robotics where several factors can cause disturbances to the system's dynamics. This work addresses the problem of identifying multiple contexts of an AUV model. We build a simulation model of the robot from experimental data, and use it to fill in the missing data and generate different model contexts. We implement an architecture based on long-short-term-memory (LSTM) networks to learn the different contexts directly from the data. We show that the LSTM network can achieve high classification accuracy compared to baseline methods, showing robustness against noise and scaling efficiently on large datasets.