November 12, 2018 Journal article Open Access

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

Citation Style Language JSON Export

{
  "DOI": "10.1109/TRO.2018.2878318", 
  "author": [
    {
      "family": "Thuruthel, Thomas George"
    }, 
    {
      "family": "Falotico, Egidio"
    }, 
    {
      "family": "Renda, Federico"
    }, 
    {
      "family": "Laschi, Cecilia"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2018, 
        11, 
        12
      ]
    ]
  }, 
  "abstract": "<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>", 
  "title": "Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators", 
  "type": "article-journal", 
  "id": "3759636"
}