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2 D Subspaces for User-Driven Robot Grasping

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 control subspaces embedded within the pose space of a robotic system. Using captured human motion for training, we address 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 data, we introduce a method for denoising neighborhood graphs for embedding hand motion into 2D spaces. We present results demonstrating our approach to interactive sparse control for successful power grasping and precision grasping using a 13 DOF robot hand.