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Proprioceptive Inference for Dual-Arm Grasping of Bulky Objects Using RoboSimian

Show simple item record Burkhardt, Matt Karumanchi, Sisir Edelberg, Kyle Burdick, Joel W. Backes, Paul 2020-04-22T22:49:34Z 2020-04-22T22:49:34Z 2018-05-21
dc.identifier.citation 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018 en_US
dc.identifier.clearanceno 18-0796
dc.description.abstract This work demonstrates dual-arm lifting of bulky objects based on inferred object properties (center of mass (COM) location, weight, and shape) using proprioception (i.e. force torque measurements). Data-driven Bayesian models describe these quantities, which enables subsequent behaviors to depend on confidence of the learned models. Experiments were conducted using the NASA Jet Propulsion Laboratory’s (JPL) RoboSimian to lift a variety of cumbersome objects ranging in mass from 7kg to 25kg. The position of a supporting second manipulator was determined using a particle set and heuristics that were derived from inferred object properties. The supporting manipulator decreased the initial manipulator’s load and distributed the wrench load more equitably across each manipulator, for each bulky object. Knowledge of the objects came from pure proprioception (i.e. without reliance on vision or other exteroceptive sensors) throughout the experiments. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2018 en_US
dc.title Proprioceptive Inference for Dual-Arm Grasping of Bulky Objects Using RoboSimian en_US
dc.type Preprint en_US

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