Humanoid batting with bipedal balancing

This paper reports our first attempt to achieve a baseball batting demonstration with a human-sized bipedal humanoid robot, aimed at presenting a performance integrating perception, control and learning. Real-time whole-body motion control and visual perception are integrated to allow the robot to predict the ball position and hit it. The ball was thrown by a human and recognized by the eye cameras. For the prediction, we propose a simple sequential estimator to predict the arrival time and position of the ball. For the control, fast and smooth batting trajectories are superposed on a whole-body force controller taking account of bipedal balancing. Although the prediction and learning model are not fully implemented, this paper demonstrates promising simulation and experimental results with the proposed framework. So far, we have succeeded at the timing prediction and fast swing motion within 300 ms without falling. Experimental and simulation videos are available as supplementary material.