Multi-Robot Cooperative Spatial Reasoning with Multimodal Large Language Models

Researchers introduced CoopSR, the first benchmark for multi-robot cooperative spatial reasoning, along with the EgoTeam dataset containing 114,227 question-answer pairs across 19 question types. The team also proposed SP-CoR, a multimodal large language model framework that integrates synchronized egocentric videos from multiple robots to answer spatial, temporal, and coordination questions, outperforming the strongest fine-tuned baseline by up to 7.12% in simulated environments. The work advances embodied AI by enabling models to reason cooperatively from multiple robotic viewpoints without requiring privileged pose data at test time.

Computer Science Computer Vision and Pattern Recognition Submitted on 18 May 2026 v1 https://arxiv.org/abs/2605.18431v1 , last revised 19 May 2026 this version, v2 Title:Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models View PDF /pdf/2605.18431 HTML experimental https://arxiv.org/html/2605.18431v2 Abstract:Multimodal Large Language Models MLLMs have made substantial progress in egocentric video understanding, but their ability to reason cooperatively from multiple embodied viewpoints remains largely unexplored. We study this problem through multi-robot cooperative dynamic spatial reasoning, where a model must answer spatial, temporal, visibility, and coordination questions by integrating synchronized egocentric videos from a team of moving robots. To support this setting, we introduce CoopSR, the first benchmark for this task, together with EgoTeam, a multi-robot egocentric QA dataset. EgoTeam contains 114,227 QA pairs spanning 19 question types, four difficulty tiers, and three team sizes in Habitat and iGibson, along with a real-world test set of around 2,326 QAs collected using two quadruped robots. We further propose SP-CoR Spectral and Physics-Informed Cooperative Reasoner , an MLLM framework for fine-grained cooperative spatial reasoning. SP-CoR combines dynamics-aware multi-robot frame sampling, spectral- and physics-guided view fusion, and physics-aligned prompt distillation, enabling the model to benefit from privileged robot-pose supervision during training while requiring only egocentric videos at test time. Across 22 MLLM baselines, SP-CoR consistently improves cooperative reasoning, outperforming the strongest fine-tuned baseline by +3.87% on Habitat and +7.12% on iGibson. It also shows stronger generalization to unseen team sizes and real-world robot tests. Code can be found at this https URL . Submission history From: Kunyu Peng view email /show-email/a3c8df04/2605.18431 Mon, 18 May 2026 14:04:26 UTC 18,895 KB v1 /abs/2605.18431v1 v2 Tue, 19 May 2026 05:12:41 UTC 18,895 KB References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .