A Distributed Gaussian Process Model for Multi-Robot Mapping
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912951785488384 |
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| author | Nabarro, Seth van der Wilk, Mark Davison, Andrew J. |
| author_facet | Nabarro, Seth van der Wilk, Mark Davison, Andrew J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07351 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Distributed Gaussian Process Model for Multi-Robot Mapping Nabarro, Seth van der Wilk, Mark Davison, Andrew J. Robotics Machine Learning 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. |
| title | A Distributed Gaussian Process Model for Multi-Robot Mapping |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2603.07351 |