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Sparse control for high-DOF assistive robots

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Abstract

Human control of high degree-of-freedom robotic systems can often be difficult due to a sparse control problem. This problem relates to the disparity in the amount of information required by the robot’s control variables and the relatively small volume of information a human can specify. To address the sparse control problem, we propose the use of subspaces embedded within the pose space of a robotic system. Driven by human motion, our approach is to uncover 2D subspaces using dimension reduction techniques that allow cursor control, or eventually decoding of neural activity, to drive a robotic hand. 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. The use of shape descriptors for representing hand pose is additionally explored for dealing with occluded parts of the hand during data collection. We demonstrate the use of 2D subspaces of power and precision grasps for driving a physically simulated hand from 2D mouse input.

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Authors and Affiliations

  1. Department of Computer Science, Brown University, 115 Waterman St., Providence, RI, 02912-1910, USA

    Odest Chadwicke Jenkins

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  1. Odest Chadwicke Jenkins
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Correspondence to Odest Chadwicke Jenkins.

Additional information

This work was supported in part by ONR Award N000140710141, ONR DURIP “Instrumentation for Modeling Dexterous Manipulation”, and NSF Award IIS-0534858.

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Jenkins, O.C. Sparse control for high-DOF assistive robots. Intel Serv Robotics 1, 135–141 (2008). https://doi.org/10.1007/s11370-007-0013-0

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