SDHN: Skewness-Driven Hypergraph Networks for Enhanced Localized Multi-Robot Coordination
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908473203097600 |
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| author | Zhao, Delin Shan, Yanbo Liu, Chang Lin, Shenghang Shou, Yingxin Xu, Bin |
| author_facet | Zhao, Delin Shan, Yanbo Liu, Chang Lin, Shenghang Shou, Yingxin Xu, Bin |
| contents | Multi-Agent Reinforcement Learning is widely used for multi-robot coordination, where simple graphs typically model pairwise interactions. However, such representations fail to capture higher-order collaborations, limiting effectiveness in complex tasks. While hypergraph-based approaches enhance cooperation, existing methods often generate arbitrary hypergraph structures and lack adaptability to environmental uncertainties. To address these challenges, we propose the Skewness-Driven Hypergraph Network (SDHN), which employs stochastic Bernoulli hyperedges to explicitly model higher-order multi-robot interactions. By introducing a skewness loss, SDHN promotes an efficient structure with Small-Hyperedge Dominant Hypergraph, allowing robots to prioritize localized synchronization while still adhering to the overall information, similar to human coordination. Extensive experiments on Moving Agents in Formation and Robotic Warehouse tasks validate SDHN's effectiveness, demonstrating superior performance over state-of-the-art baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06684 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SDHN: Skewness-Driven Hypergraph Networks for Enhanced Localized Multi-Robot Coordination Zhao, Delin Shan, Yanbo Liu, Chang Lin, Shenghang Shou, Yingxin Xu, Bin Robotics Multiagent Systems Multi-Agent Reinforcement Learning is widely used for multi-robot coordination, where simple graphs typically model pairwise interactions. However, such representations fail to capture higher-order collaborations, limiting effectiveness in complex tasks. While hypergraph-based approaches enhance cooperation, existing methods often generate arbitrary hypergraph structures and lack adaptability to environmental uncertainties. To address these challenges, we propose the Skewness-Driven Hypergraph Network (SDHN), which employs stochastic Bernoulli hyperedges to explicitly model higher-order multi-robot interactions. By introducing a skewness loss, SDHN promotes an efficient structure with Small-Hyperedge Dominant Hypergraph, allowing robots to prioritize localized synchronization while still adhering to the overall information, similar to human coordination. Extensive experiments on Moving Agents in Formation and Robotic Warehouse tasks validate SDHN's effectiveness, demonstrating superior performance over state-of-the-art baselines. |
| title | SDHN: Skewness-Driven Hypergraph Networks for Enhanced Localized Multi-Robot Coordination |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2504.06684 |