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Open Access item Robot learning from demonstration of force-based tasks with multiple solution trajectories

Authors:Rozo, Leonel
Jiménez Schlegl, Pablo
Torras, Carme
Keywords:Robot learning, Human-robot interaction, HMM, GMR
Issue Date:2011
Publisher:Institute of Electrical and Electronics Engineers
Citation:15th International Conference on Advanced Robotics: 124-129 (2011)
Abstract:A learning framework with a bidirectional communication channel is proposed, where a human performs several demonstrations of a task using a haptic device (providing him/her with force-torque feedback) while a robot captures these executions using only its force-based perceptive system. Our work departs from the usual approaches to learning by demonstration in that the robot has to execute the task blindly, relying only on force-torque perceptions, and, more essential, we address goal-driven manipulation tasks with multiple solution trajectories, whereas most works tackle tasks that can be learned by just finding a generalization at the trajectory level. To cope with these multiple-solution tasks, in our framework demonstrations are represented by means of a Hidden Markov Model (HMM) and the robot reproduction of the task is performed using a modified version of Gaussian Mixture Regression that incorporates temporal information (GMRa) through the forward variable of the HMM. Also, we exploit the haptic device as a teaching and communication tool in a human-robot interaction context, as an alternative to kinesthetic-based teaching systems. Results show that the robot is able to learn a container-emptying task relying only on force-based perceptions and to achieve the goal from several non-trained initial conditions.
Description:Presentado al ICAR 2011 celebrado en Tallin (Estonia) del 20 al 23 de junio de 2011.
Publisher version (URL):http://dx.doi.org/10.1109/ICAR.2011.6088633
Appears in Collections:(IRII) Libros y partes de libros

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