VLAモデルの内部表現から記号的状態を探り認知アーキテクチャへ統合する
Probing a Vision-Language-Action Model for Symbolic States and Integration into a Cognitive Architecture
OpenVLAの隠れ層を解析し、物体・関係・行動状態の記号的表現が高精度で符号化されていることを示すとともに、認知アーキテクチャDIARCと統合して解釈性と堅牢性を高める手法を提案した。
著者: Hong Lu, Hengxu Li, Prithviraj Singh Shahani, Stephanie Herbers, Matthias Scheutz
分類: cs.RO, cs.AI
原文アブストラクト
Vision-language-action (VLA) models hold promise as generalist robotics solutions by translating visual and linguistic inputs into robot actions, yet they lack reliability due to their black-box nature and sensitivity to environmental changes. In contrast, cognitive architectures (CA) excel in symbolic reasoning and state monitoring but are constrained by rigid predefined execution. This work bridges these approaches by probing OpenVLA's hidden layers to uncover symbolic representations of object properties, relations, and action states, enabling integration with a CA for enhanced interpretability and robustness. Through experiments on LIBERO-spatial pick-and-place tasks, we analyze the encoding of symbolic states across different layers of OpenVLA's Llama backbone. Our probing results show consistently high accuracies (> 0.90) for both object and action states across most layers, though contrary to our hypotheses, we did not observe the expected pattern of object states being encoded earlier than action states. We demonstrate an integrated DIARC-OpenVLA system that leverages these symbolic representations for real-time state monitoring, laying the foundation for more interpretable and reliable robotic manipulation.
