AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Learning Collaborative Impedance-Based Robot Behaviors
Leonel Dario Rozo, Sylvain Calinon, Darwin Caldwell, Pablo Jimenez, Carme Torras

Last modified: 2013-06-29


Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans in a safe and natural way to accomplish tasks cooperatively. Robots with variable impedance capabilities opens the door to new challenging applications, where the learning algorithms must be extended by encapsulating force and vision information. In this paper we propose a framework to transfer impedance-based behaviors to a torque-controlled robot by kinesthetic teaching. The proposed model encodes the examples as a task-parameterized statistical dynamical system, where the robot impedance is shaped by estimating virtual stiffness matrices from the set of demonstrations. A collaborative assembly task is used as testbed. The results show that the model can be used to modify the robot impedance along task execution to facilitate the collaboration, by triggering stiff and compliant behaviors in an on-line manner to adapt to the user's actions.


Physical human-robot interaction; collaborative manipulation; impedance learning; task-parameterized model

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