Sparse Control of Robot Grasping from 2D Subspaces

Human control of high degree-of-freedom robotic systems is often difficult due to the overwhelming number of variables that need to be specified. Instead, we propose the use of sparse subspaces embedded within the pose space of a robotic system. Driven by human motion, we addressed this sparse control problem by uncovering 2D subspaces that allow cursor control, or eventually decoding of neural activity, to drive a robotic hand. Considering the problems in previous work related to noise in pose graph construction and motion capture, we introduced a method for denoising neighborhood graphs for embedding hand motion into 2D spaces. Such spaces allow for control of high-DOF systems using 2D interfaces such as cursor control via mouse or decoding of neural activity. We present results demonstrating our approach to interactive sparse control for successful power grasping and precision grasping using a 13 DOF robot hand.