Please use this identifier to cite or link to this item:
Título : Robot learning from demonstration of force-based tasks with multiple solution trajectories
Autor : Rozo, Leonel, Jiménez Schlegl, Pablo, Torras, Carme
Palabras clave : Robot learning
Human-robot interaction
Fecha de publicación : 2011
Editor: Institute of Electrical and Electronics Engineers
Resumen: 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.
Descripción : Presentado al ICAR 2011 celebrado en Tallin (Estonia) del 20 al 23 de junio de 2011.
Versión del editor:
ISBN : 978-1-4577-1158-9
DOI: 10.1109/ICAR.2011.6088633
Citación : 15th International Conference on Advanced Robotics: 124-129 (2011)
Appears in Collections:(IRII) Libros y partes de libros

Files in This Item:
File Description SizeFormat 
Robot_Learning_Tallin.pdf978,48 kBAdobe PDFView/Open
Show full item record

Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.