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Paper WeA01.1

Kronander, Klas (Learning Algorithms and Systems Laboratory, EPFL), Billard, Aude (EPFL)

Online Learning of Varying Stiffness through Physical Human-Robot Interaction

Scheduled for presentation during the Regular Session "Learning and Adaptation Control of Robotic Systems II" (WeA01), Wednesday, May 16, 2012, 08:30−08:45, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on November 18, 2017

Keywords Learning and Adaptive Systems, Physical Human-Robot Interaction, Compliance and Impedance Control

Abstract

Programming by Demonstration offers an intuitive framework for teaching robots how to perform various tasks without having to preprogram them. It also offers an intuitive way to provide corrections and refine teaching during task execution. Previously, mostly position constraints have been taken into account when teaching tasks from demonstrations. In this work, we tackle the problem of teaching tasks that require or can benefit from varying stiffness. This extension is not trivial, as the teacher needs to have a way of communicating to the robot what stiffness it should use. We propose a method by which the teacher can modulate the stiffness of the robot in any direction through physical interaction. The system is incremental and works online, so that the teacher can instantly feel how the robot learns from the interaction. We validate the proposed approach on two experiments on a 7-Dof Barrett WAM arm.

 

 

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