Federated Multi-Agent Mapping for Planetary Exploration
<|reference_start|>Federated Multi-Agent Mapping for Planetary Exploration: Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of spacecraft robotic systems venture to more extreme and far-flung environments. A key challenge in this new paradigm will be to effectively share and utilize the vast amount of data generated on-board while operating in bandwidth-constrained regimes such as those often found in space missions. Federated learning (FL) is a promising tool for bridging this gap for a host of tasks studied across proposed mission concepts. Drawing inspiration from the upcoming CADRE Lunar rover mission, we study the task of federated multi-agent mapping and propose an approach to jointly train a centralized map model across agents without the need to share raw data. Our approach leverages implicit neural mapping to generate parsimonious and adaptable representations. We further enhance this approach with meta-initialization on Earth datasets, pre-training the network to quickly adapt to extreme and rugged terrain. We demonstrate the efficacy of our proposed federated mapping approach using Martian terrains and glacier datasets and show how it outperforms benchmarks on map reconstruction losses as well as downstream path planning tasks.<|reference_end|>
@article{szatmari2024federated,
    title={Federated Multi-Agent Mapping for Planetary Exploration},
    author={Tiberiu-Ioan Szatmari and Abhishek Cauligi},
    journal={arXiv preprint arXiv:2404.02289},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02289},
    primaryClass={cs.RO cs.LG cs.MA}
}