抄録
We apply reinforcement learning to a wheeled inverted pendulum robot that acquires dynamic throwing motion utilizing whole body dynamics. Large number of parameters are needed to be calibrated so that the robot becomes able to throw a ball far away utilizing its own body dynamics while it keeps standing. We investigated the learning process of the throwing motion by application of a policy gradient method with a dynamics simulater.
