ロボット安全性のための検証可能な基盤モデル
Verifiable Foundation Models for Robot Safety
基盤モデルをロボット制御に使う際の安全性検証を可能にするため、ポリシーを高次元知覚を担う大きなコントローラと低次元の安全センサーに基づく小さな安全モジュールに分離するフレームワークFEARLを提案し、シミュレーションと実機で有効性を示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Davide Corsi, Kyungmin Kim, Roy Fox
分類: cs.RO, cs.LG
原文アブストラクト
Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools. In this paper, we present FEARL (Foundation-Enabled Assured Robot Learning), a framework that addresses this tension through a modular architectural decomposition. FEARL separates the policy into a large Controller (C) responsible for high-dimensional perception and task reasoning, and a small Safety module (S) that receives low-dimensional observations from dedicated safety sensors together with a bounded context embedding from C and produces the final action. Since many robot safety requirements, such as collision avoidance and workspace boundary constraints, can be expressed over these safety sensor observations, formal verification can be applied to S rather than to the full foundation-model backbone. This makes formal analysis tractable with existing tools while preserving the Controller's expressive power for task reasoning. To show that the decomposed policy remains capable of solving diverse tasks, we evaluate FEARL on three simulated robotic domains using multiple Controller backbones and training procedures, including pretrained off-the-shelf vision-language-action models. We further transfer the learned policy from one of our simulated tasks to a physical robot, suggesting that the low-dimensional safety interface supports practical sim-to-real transfer.
