A Distributed Gaussian Process Model for Multi-Robot Mapping

• URL: http://arxiv.org/abs/2603.07351v1
• Date: Sat, 07 Mar 2026 21:30:36 GMT
• Title: A Distributed Gaussian Process Model for Multi-Robot Mapping
• Abstract summary: DistGP is a multi-robot learning method for collaborative learning of a global function using only local experience and computation.<n>Our loopy model outperforms Tree-Structured GPs citebui2014tree and can be trained online and in settings with dynamic connectivity.<n>Last, we compare to DiNNO citeyu2022dinno, a distributed neural network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.
• Score: 34.711400073303174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 network (NN) optimiser, and find DistGP achieves superior accuracy, is more robust to sparse communication and is better able to learn continually.

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