Risk-Aware Co-Optimization for Multi-Robot Control Under Execution Uncertainty

Abstract

While multi-robot motion control often treats the environment as a static constraint and assumes near-perfect execution, real-world uncertainty poses significant challenges. In poorly structured or densely populated spaces, these unpredictable variables frequently lead to collision risks and degraded performance. We address this issue by proposing a risk-aware framework that co-optimizes robot actions and environment configurations to mitigate execution uncertainty. To handle the complex relationships among the environment, robots, and uncertainty impact, we develop a bi-level approach using neural surrogate optimization, effectively bridging nonlinear programming with model-free learning for joint optimization. Supported by theoretical robustness guarantees, numerical simulations, and physical experiments, our co-optimization strategy creates “multi-robot-friendly” environments that mitigate the impact of execution uncertainty and facilitate superior multi-robot coordination.