Learning 2D Subspaces for User-Controlled Robot Grasping

Human control of high degree-of-freedom robotic systems, e.g. anthropomorphic robot hands, is often difficult due to the overwhelming number of variables that need to be specified. The problem is magnified for applications to biorobotics where efforts to decode user neural activity into control signals have demonstrated success limited to 2-3 DOFs with bandwidth approximately 15 bits/sec [5]. We address this sparse control problem by learning a high-dimensional manifold of robot poses in order to provide 2D subspaces for interactive control of a high-DOF robot hand.