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Member calinon

Sylvain Calinon

Visiting researcher

Email:
sylvain.calinon_at_epfl.ch

Room:
ME A3 393

Phones:

  • Office: +39 0107 1781 755
  • Home: +41 79 368 16 64 (Sw
  • Mobile: +39 333 611 03 74 (I
  • Fax: +41 21 693 78 50

Personal Home Page:
programming-by-demonstration.org/SylvainCalinon/

Fields of Interest

  • Programming by Demonstration (PbD)
  • Learning by Imitation
  • Human-Robot Interaction (HRI)
  • Machine Learning
  • Humanoid Robots

    Robot Programming by Demonstration (PbD) covers methods by which a robot learns new skills through human guidance. Also referred to as learning by imitation or apprenticeship learning, PbD takes inspiration from the way humans learn new skills by imitation to develop methods by which new skills can be transmitted to a robot.


    PbD covers a broad range of applications. In industrial robotics, the goal is to reduce the time and costs required to program the robot. The rationale is that PbD would allow to modify an existing product, create several versions of a similar product or assemble new products in a very rapid way without using a teach pendant or a computer language. This could then be done by lay users without help from an expert in robotics.


    PbD is perceived as particularly useful to service robots, i.e. robots deemed to work in direct collaboration with humans. In this case, methods for PbD go beyond transferring skills and offer new ways for the robot to interact with the human, from being capable of recognizing people's motion to predicting their intention and seconding them in the accomplishment of complex tasks. As the technology improved to provide these robots with more and more complex hardware, including multiple sensor modalities and numerous degrees of freedom, robot control and especially robot learning became more and more complex too.


    Learning control strategies for numerous degrees of freedom platforms deemed to interact in complex and variable environments, such as households is faced with two key challenges: first, the complexity of the tasks to be learned is such that pure trial and error learning would be too slow. PbD appears thus a good approach to speed up learning by reducing the search space, while still allowing the robot to refine its model of the demonstration through trial and error. Second, there should be a continuum between learning and control, so that control strategies can adapt on the fly to drastic changes in the environment. The present work addresses both challenges in investigating methods by which PbD is used to learn the dynamics of robot's motion, and, by so doing, provide the robot with a generic and adaptive model of control.

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Last update: 11/03/08