Robot Grasping for Prosthetic Applications

Neurally controlled prosthetic devices capable of environmental manipulation have much potential towards restoring the physical functionality of disabled people. However, the number of user input variables provided by current neural decoding systems is much less than the number of control degrees-of-freedom (DOFs) of a prosthetic hand and/or arm. To address this sparse control problem, we propose the use of low-dimensional subspaces embedded within the pose space of a robotic limb. These subspaces are extracted using dimension reduction techniques to compress captured human hand motion into a (often two-dimensional) subspace that can be spanned by the output of neural decoding systems. To evaluate our approach, we explore a set of current state-of-the-art dimension reduction techniques and show results for effective control of a 13 DOF robot hand performing basic grasping tasks taking place in both static and dynamic environments.