Learning of Robot Safety Policies via Adversarial Synthetic Scenarios 事件

Learning of Robot Safety Policies via Adversarial Synthetic Scenarios arXiv:2606.05952v1 Announce Type: cross Abstract: In this work, we propose an agentic gamification framework for hazard-informed learning of robot safety policies through synthetic scenarios. We model scenario generation as an adversarial game between two agents: a Red Team that explores the space of potential failures by constructing hazardous situations, and a Blue Team that incrementally refines safety policies to prevent