November 12, 2018 Journal article Open Access

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

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    "description": "<p>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.</p>", 
    "license": {
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    "title": "Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators", 
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    "publication_date": "2018-11-12", 
    "creators": [
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        "affiliation": "BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy", 
        "name": "Thuruthel, Thomas George"
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      {
        "affiliation": "BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy", 
        "name": "Falotico, Egidio"
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      {
        "affiliation": "Department of Mechanical Engineering and the Center for Autonomous Robotics Systems, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates", 
        "name": "Renda, Federico"
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        "affiliation": "BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy", 
        "name": "Laschi, Cecilia"
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