A Distributed Gaussian Process Model for Multi-Robot Mapping
We propose DistGP: a multi-robot learning method for collaborative learning of a global function using only local experience and computation. We utilise a sparse Gaussian process (GP) model with a factorisation that mirrors the multi-robot structure of the task, and admits distributed training via Gaussian belief propagation (GBP). Our loopy model outperforms Tree-Structured GPs \cite{bui2014tree} and can be trained online and in settings with dynamic connectivity. We show that such distributed, asynchronous training can reach the same performance as a centralised, batch-trained model, albeit with slower convergence. Last, we compare to DiNNO \cite{yu2022dinno}, a distributed neural netwo...
| Authors | Seth Nabarro, Mark van der Wilk, Andrew J. Davison |
|---|---|
| Primary category | cs.RO |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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