意味的安全ロボット制御のためのニューロシンボリック述語学習
Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control
人間が指定した安全知識から再利用可能な安全表現を学習する微分可能推論器NEUPROを提案し、解釈可能なロボット安全仕様のベンチマークREASONを構築した。
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: Zihan Ye, Jiayi Liu, Puze Liu, Jiayun Li, Georgia Chalvatzaki, Jan Peters, Kristian Kersting
分類: cs.RO
原文アブストラクト
As robots are increasingly deployed in everyday environments, ensuring their safety has become a central challenge. Existing methods often encode safety requirements as opaque mathematical/logical formulations or dense cost functions. While effective in specific tasks, they remain difficult to interpret, tightly coupled to individual tasks, and offer limited insight into why a robot action is considered safe or unsafe. To address this limitation, we propose ``Neuro-Symbolic Predicate Learning for Semantic Safe Robot Control'' (NEUPRO), which leverages a differentiable reasoner that can learn reusable safety representations from human-specified safety knowledge. NEUPRO allows practitioners to express task-related safety requirements as transparent symbolic rules, while enabling gradients to propagate through these rules to a feature extractor that maps raw observations to safety-relevant concepts. As a result, the learned feature extractor is (softly) grounded in human-understandable semantics, supports transparent constraint evaluation, and is transferable across tasks. By coupling interpretability with differentiability, NEUPRO moves beyond opaque cost design toward reusable safety reasoning. To evaluate NEUPRO's capability, we collect and release REASON, the first real robot benchmark dataset for interpretable robot safety specification. Experiments on REASON show that NEUPRO learns safety-critical features that generalize across tasks, mitigate the interpretability limitations of conventional black-box cost formulations, and provide explicit explanations of safety violation.
