幾何学的に解釈可能なニューラル知覚によるビジュアルサーボ
Visual Servoing with Geometrically Interpretable Neural Perception
ビジュアルサーボの知覚を深層学習で解く手法を提案し、制御則と特徴運動モデルを教師信号に使うことで推定特徴に幾何学的解釈を与え、知覚と制御を分離したまま柔軟で解釈可能な枠組みを実現した。
著者: Antonio Paolillo, Mirko Nava, Dario Piga, Alessandro Giusti
分類: cs.RO
原文アブストラクト
An increasing number of nonspecialist robotic users demand easy-to-use machines. In the context of visual servoing, the removal of explicit image processing is becoming a trend, allowing an easy application of this technique. This work presents a deep learning approach for solving the perception problem within the visual servoing scheme. An artificial neural network is trained using the supervision coming from the knowledge of the controller and the visual features motion model. In this way, it is possible to give a geometrical interpretation to the estimated visual features, which can be used in the analytical law of the visual servoing. The approach keeps perception and control decoupled, conferring flexibility and interpretability on the whole framework. Simulated and real experiments with a robotic manipulator validate our approach.
