Multi-robot navigation improves safety using shared uncertain forecasts
CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
RoboticsArtificial IntelligenceMachine Learning
Summary
Navigating robots safely around people and other robots is hard, especially when obstacles block their view or the environment changes unexpectedly. The authors propose a method where robots share information to better predict where others will be, including understanding how uncertain these predictions might be. This helps robots make safer movement choices by avoiding areas likely to be occupied or risky. Their approach works better than previous ones in simulations and real-world tests with multiple robots and crowds.
What this means in practice
- •For robotics engineers: Develop multi-robot systems that plan safer paths in crowded and dynamic human environments by sharing uncertain future occupancy predictions during navigation.
- •For warehouse automation teams: Improve coordination and risk-aware path planning for fleets of mobile robots operating around people and obstacles in changing warehouse layouts.
Authors
Zehao Wang, Jianpeng Yao, Yuping Wang, Yiping Li, Jiaqi Li, Zhixu Li, Kanghoon Lee, Jinfeng Wang, Cong Liu, Zhaowei Tan, Jinkyoo Park, Jiachen Li
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/.