Motion control for a hitting task: A learning approach to inverse mapping
説明
We describe our approach to the robot's Hanetsuki task (Japanese badminton), that is, to return the incoming ball to the human opponent with a racket. A learning algorithm that consists of updating action commands and smoothing them based on the Gaussian kernel is proposed to compensate for the insufficiency in a model-based approach. Experimental results obtained by using the developed Hanetsuki robot are also shown.