身体化パーセプトロン表現に基づく神経・筋肉ネットワークの共設計
Co-design of Neural and Muscle Network based on Embodied Perceptron Representation
身体をニューラルネットワークとして捉える理論的枠組み「Embodied Perceptron」を提案し、筋骨格ロボットの制御器と筋肉配置を同時最適化することで、単一ニューロン制御でも安定性と学習効率が向上することを示した。
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著者: Siyuan Tao, Yoichi Masuda, Hiroyuki Nabae, Masato Ishikawa
分類: cs.RO
原文アブストラクト
Recent advances in AI technologies have enabled the advanced design of complex control policies. In contrast, focusing on the body, many robots still employ simple bodies that can limit adaptability to environments. Studies in embodied robotics have shown that well-designed bodies can partially replace the role of control and computation with physical body-environment interactions, yet such designs still depend heavily on expert intuition. There is a need for a systematic theoretical framework for body design, as well as a method for joint optimization of the body and controller. To address this, we introduce the Embodied Perceptron, a theoretical framework that unifies neural networks and physical body systems. In this view, the body itself acts as a perceptron: mechanical parameters correspond to weights, and physical nonlinearities play the role of activation functions. By representing physical constraints as weights and nonlinear properties as activation functions, a physical body can be modeled in neural-network form. The system representation enables us to explicitly and theoretically explain that the body can substitute for part of the neural control. As an application, we co-optimize control policy and muscle configuration in a musculoskeletal robot and show that the resulting embodied intelligence can provide inherent stability, improve learning efficiency, and drastically reduce model size-even with a single-neuron controller. The results bridge the informational and physical worlds and provide a pathway toward understanding and systematic design of embodied AI systems.