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      • Department of Electrical and Electronics Engineering
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      Functional contour-following via haptic perception and reinforcement learning

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      Author(s)
      Hellman, R. B.
      Tekin, Cem
      Schaar, M. V.
      Santos, V. J.
      Date
      2018
      Source Title
      IEEE Transactions on Haptics
      Print ISSN
      1939-1412
      Electronic ISSN
      2329-4051
      Publisher
      Institute of Electrical and Electronics Engineers
      Volume
      11
      Issue
      1
      Pages
      61 - 72
      Language
      English
      Type
      Article
      Item Usage Stats
      189
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      260
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      Abstract
      Many tasks involve the fine manipulation of objects despite limited visual feedback. In such scenarios, tactile and proprioceptive feedback can be leveraged for task completion. We present an approach for real-time haptic perception and decision-making for a haptics-driven, functional contour-following task: The closure of a ziplock bag. This task is challenging for robots because the bag is deformable, transparent, and visually occluded by artificial fingertip sensors that are also compliant. A deep neural net classifier was trained to estimate the state of a zipper within a robot's pinch grasp. A Contextual Multi-Armed Bandit (C-MAB) reinforcement learning algorithm was implemented to maximize cumulative rewards by balancing exploration versus exploitation of the state-action space. The C-MAB learner outperformed a benchmark Q-learner by more efficiently exploring the state-action space while learning a hard-to-code task. The learned C-MAB policy was tested with novel ziplock bag scenarios and contours (wire, rope). Importantly, this work contributes to the development of reinforcement learning approaches that account for limited resources such as hardware life and researcher time. As robots are used to perform complex, physically interactive tasks in unstructured or unmodeled environments, it becomes important to develop methods that enable efficient and effective learning with physical testbeds.
      Keywords
      Active touch
      Contour-following
      Decision making
      Haptic perception
      Manipulation
      Reinforcement learning
      Permalink
      http://hdl.handle.net/11693/50279
      Published Version (Please cite this version)
      https://doi.org/10.1109/TOH.2017.2753233
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      • Department of Electrical and Electronics Engineering 3863
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