Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
Nikolai Dorofeev, Alexey Odinokov, Rostislav Yavorskiy
Why It Matters
What makes this one worth your time
This research addresses the critical need for robust safety mechanisms in robots operating in unpredictable real-world environments, which is essential for their safe deployment.
A novel approach to enhancing robot safety through adversarial scenario generation.
Summary
The paper proposes a framework for learning robot safety policies through adversarial scenario generation, involving a Red Team that creates hazardous situations and a Blue Team that refines safety policies to mitigate risks.
Key contributions
- Introduction of an agentic gamification framework for hazard-informed learning.
- Problem formulation for the adversarial scenario generation process.
- Proposed solution architecture for integrating safety policies into robotic systems.
Notable insights
- The use of an adversarial game framework to model scenario generation allows for a more dynamic exploration of potential failures compared to traditional methods.
- Combining classical risk modeling with modern learning paradigms may yield a more comprehensive understanding of safety in complex systems.
Possible limitations
- Not stated in the abstract.
Abstract
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 them. This iterative process enables efficient discovery of high-risk edge cases that are unlikely to be captured through random simulation or manual enumeration. By combining classical risk modeling with adversarial scenario generation and modern learning paradigms, this work provides a scalable pathway for embedding safety into Physical AI systems operating in complex real-world environments. The paper describes ongoing work. The contribution is a problem formulation and a proposed solution architecture.