CoopUQ：用不确定性感知的协同预测做多机器人社会导航
CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
一篇多机器人导航的新论文，思路是把多机协同的占据预测和不确定性一起喂给强化学习策略，OOD 场景下更稳，代码开源可以直接复现。
arXiv 论文提出 CoopUQ，一个多机器人社会导航框架。它通过跨机器人的时空特征融合预测未来占据栅格地图，再用约束式多智能体强化学习把预测结果及其不确定性引入策略，对预测为占据或高不确定性的区域施加代价。在 Unity3D 仿真中测试了多种布局、行人密度和行人冲刺行为，涵盖分布内和 OOD 场景，预测性能和安全性都优于代表性基线。代码和视频已在 coopuq.github.io 开源，真机实验验证了跨场景的鲁棒性。
CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
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/.