敵対的合成シナリオによるロボット安全ポリシーの学習
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
赤チームが危険な状況を生成し、青チームが安全ポリシーを強化する敵対的ゲームを通じて、ロボットの安全ポリシーを効率的に学習する枠組みを提案した論文。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Nikolai Dorofeev, Alexey Odinokov, Rostislav Yavorskiy
分類: cs.RO, cs.AI
原文アブストラクト
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.
