Newest papers

CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting

Zehao Wang, Jianpeng Yao, Yuping Wang, Yiping Li, Jiaqi Li, Zhixu Li, Kanghoon Lee, Jinfeng Wang, Cong Liu, Zhaowei Tan, Jinkyoo Park, Jiachen Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26852 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

强化学习使移动机器人能够在受限的人类共享环境中做出快速、可扩展的导航决策。然而,直接扩展单机器人行为方法到多机器人设置往往无法确保安全性和效率,特别是在隐患和分布外(OOD)场景下,单个机器人可能会忽略潜在风险或做出不准确的预测。通过跨机器人空间和航天功能的合并有效地利用合作仍然是一个重大挑战。我们建议CoCoUQ,一个多机器人社交导航框架,通过受限多强化学习来指导政策,在那里跨越各种环境密度的合作占用预测和强化决策。CoUQ预测未来占用网地图,通过跨机器人空间和航天功能的合并,并进一步推动预测和我们的机器人不确定性,其中包括其多

Machine translation (Meta M2M100); the English original follows.

Abstract

Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging cooperation effectively for both prediction and navigation remains a major challenge. We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making. CoopUQ predicts future occupancy grid maps through cross-robot spatiotemporal feature fusion and further leverages forecasts and their uncertainty to guide policy via constrained multi-agent reinforcement learning, where the cost penalizes navigation through regions predicted to be occupied or highly uncertain. This design enables the policy to adapt its risk sensitivity under distribution shifts. Experimental results across multiple Unity3D-based simulation environments with both in-distribution and OOD settings, including diverse layouts, pedestrian densities, and rushing behavior, demonstrate improved prediction performance and safety over representative baselines. Real-robot experiments further demonstrate its robustness across various scenes. Our code and videos are available at https://coopuq.github.io/.

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Topics: Computer science, Robustness (evolution), Occupancy grid mapping, Reinforcement learning, Scalability, Pedestrian

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange