Learning the Stiffness of a Continuous Soft Manipulator from Multiple Demonstrations

  • Danilo Bruno
  • Sylvain Calinon
  • Milad S. Malekzadeh
  • Darwin G. Caldwell
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9246)


Continuous soft robots are becoming more and more widespread in applications, due to their increased safety and flexibility in critical applications. The possibility of having soft robots that are able to change their stiffness in selected parts can help in situations where higher forces need to be applied. This paper describes a theoretical framework for learning the desired stiffness characteristics of the robot from multiple demonstrations. The framework is based on a statistical mathematical model for encoding the motion of a continuous manipulator, coupled with an optimal control strategy for learning the best impedance parameters of the manipulator.


Gaussian Mixture Model Model Predictive Control Kinematic Chain Linear Quadratic Regulator Impedance Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Danilo Bruno
    • 1
  • Sylvain Calinon
    • 1
    • 2
  • Milad S. Malekzadeh
    • 1
  • Darwin G. Caldwell
    • 1
  1. 1.Department of Advanced RoboticsIstituto Italiano di Tecnologia (IIT)GenovaItaly
  2. 2.Idiap Research InstituteMartignySwitzerland

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