2D Subspaces for Sparse Control of High-DOF Robots

Abstract

We investigate the use of five dimension reduction and manifold learning techniques to estimate a 2D subspace of hand poses for the purpose of generating motion. Our aim is to uncover a 2D parameterization from optical motion capture data that allows for transformation sparse user input trajectories into desired hand movements. The use of shape descriptors for representing hand pose is additionally explored for dealing with occluded parts of the hand during data collection. We present early results from uncovering 2D parameterizations of power and precision grasps and their use to drive a physically simulated hand from 2D mouse input.