November 12, 2018 Journal article Open Access

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

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    <subfield code="a">&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;</subfield>
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    <subfield code="u">BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy</subfield>
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    <subfield code="u">Department of Mechanical Engineering and the Center for Autonomous Robotics Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates</subfield>
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    <subfield code="u">BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy</subfield>
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    <subfield code="a">10.1109/TRO.2018.2878318</subfield>
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    <subfield code="a">Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators</subfield>
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