予測による適応：マルチセンサ能動推論トルク制御
Adaptation through prediction: multisensory active inference torque control
予測脳仮説に基づくマルチセンサ能動推論トルク制御器を提案し、7自由度ロボットアームで外乱や慣性変化に再学習なしで適応できることを示した。
著者: Cristian Meo, Giovanni Franzese, Corrado Pezzato, Max Spahn, Pablo Lanillos
分類: cs.RO, cs.AI
原文アブストラクト
Adaptation to external and internal changes is major for robotic systems in uncertain environments. Here we present a novel multisensory active inference torque controller for industrial arms that shows how prediction can be used to resolve adaptation. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches by incorporating learning and multimodal integration of low and high-dimensional sensor inputs (e.g., raw images) while simplifying the architecture. We performed a systematic evaluation of our model on a 7DoF Franka Emika Panda robot arm by comparing its behavior with previous active inference baselines and classic controllers, analyzing both qualitatively and quantitatively adaptation capabilities and control accuracy. Results showed improved control accuracy in goal-directed reaching with high noise rejection due to multimodal filtering, and adaptability to dynamical inertial changes, elasticity constraints and human disturbances without the need to relearn the model nor parameter retuning.
