Neighborhood denoising for learning high-dimensional grasping manifolds

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. Previous work has addressed this sparse control problem by learning a high-dimensional manifold of robot poses to provide low-dimensional control subspaces. Such subspaces allow cursor control, or eventually decoding of neural activity, to drive a robotic hand. Considering previously identified problems related to noise in manifold learning, we introduce a method for denoising neighborhood graphs in order to embed hand motion into 2D spaces. We present results demonstrating our approach in the case of a synthetic swissroll as well as in the embeddings for interactive sparse control for several grasping tasks.