CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative Forecasting
We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making.
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Key points
- Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments.
- Leveraging cooperation effectively for both prediction and navigation remains a major challenge.
- 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.
- 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.
Sources (1)
- [1]CoopUQ: Robust Multi-Robot Social Navigation via Uncertainty-Aware Cooperative ForecastingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 05:59 PM
We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making.
Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments.
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