Journal article Open Access

Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

Thuruthel, Thomas George; Falotico, Egidio; Renda, Federico; Laschi, Cecilia

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      <creatorName>Thuruthel, Thomas George</creatorName>
      <givenName>Thomas George</givenName>
      <affiliation>BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy</affiliation>
      <creatorName>Falotico, Egidio</creatorName>
      <affiliation>BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy</affiliation>
      <creatorName>Renda, Federico</creatorName>
      <affiliation>Department of Mechanical Engineering and the Center for Autonomous Robotics Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates</affiliation>
      <creatorName>Laschi, Cecilia</creatorName>
      <affiliation>BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy</affiliation>
    <title>Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators</title>
    <date dateType="Issued">2018-11-12</date>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TRO.2018.2878318</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads.&lt;/p&gt;</description>
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