Rozo, L., Silvério, J., Calinon, S. and Caldwell, D.G. (2016)
Exploiting Interaction Dynamics for Learning Collaborative Robot Behaviors
Interactive Machine Learning Workshop, Intl Joint Conf. on Artificial Intelligence (IJCAI).
Abstract
The new generation of smarter and safer robots aimed at assisting humans in industries and homes demands to innovate the ways in which these machines are designed and controlled. In this context, one of the biggest challenges is to empower these collaborative robots with a wide range of learning and adaptation capabilities so that they can easily assist humans in a vast variety of scenarios, ranging from assembly lines to health-care facilities. In this paper we propose to teach a collaborative robot reactive and proactive behaviors that exploit the interaction dynamics between the robot and the user. We call the proposed approach Adaptive Duration Hidden-Semi Markov Model (ADHSMM) that enables the robot to both react to the user actions and lead the task when needed. ADHSMM is used to retrieve a sequence of states governing a trajectory optimization technique that provides the reference and gain matrices to the robot controller. The proposed framework is tested in handover and transportation tasks using a 7 DOF backdrivable manipulator.
Bibtex reference
@misc{Rozo16IJCAI,
author="Rozo, L. and Silv\'erio, J. and Calinon, S. and Caldwell, D. G.",
title="Exploiting Interaction Dynamics for Learning Collaborative Robot Behaviors",
booktitle="Interactive Machine Learning Workshop, Intl Joint Conf. on Artificial Intelligence",
year="2016",
month="July",
pages="1--7"
}