Learning Inverse Kinematics for Pose-Constraint Bi-manual Movements

We present a neural network approach to learn inverse kinematics of the humanoid robot ASIMO, where we focus on bi-manual tool use. The learning copes with both the highly redundant inverse kinematics of ASIMO and the additional arbitrary constraint imposed by the tool that couples both hands. We show that this complex kinematics can be learned from few ground-truth examples using an efficient recurrent reservoir framework, which has been introduced previously for kinematics learning and movement generation. We analyze and quantify the network’s generalization for a given tool by means of reproducing the constraint in untrained target motions.