Learning Flexible Full Body Kinematics for Humanoid Tool Use

Abstract

We show that inverse kinematics of different tools can be efficiently learned with a single recurrent neural network. Our model exploits all upper body degrees of freedom of the Honda's humanoid robot research platform. Both hands are controlled at the same time with parametrized tool geometry. We show that generalization both in space as well as across tools is possible from very few training data. The network even permits extrapolation beyond the training data. For training we use an efficient online scheme for recurrent reservoir networks utilizing supervised back propagation-decor relation (BPDC) output adaptation and an unsupervised intrinsic plasticity (IP) reservoir optimization.