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Force-based robot learning of pouring skills using parametric hidden Markov models

AuthorsRozo, Leonel ; Jiménez Schlegl, Pablo ; Torras, Carme
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers
Citation9th Workshop on Robot Motion and Control: 227-232 (2013)
AbstractRobot learning from demonstration faces new challenges when applied to tasks in which forces play a key role. Pouring liquid from a bottle into a glass is one such task, where not just a motion with a certain force profile needs to be learned, but the motion is subtly conditioned by the amount of liquid in the bottle. In this paper, the pouring skill is taught to a robot as follows. In a training phase, the human teleoperates the robot using a haptic device, and data from the demonstrations are statistically encoded by a parametric hidden Markov model, which compactly encapsulates the relation between the task parameter (dependent on the bottle weight) and the force-torque traces. Gaussian mixture regression is then used at the reproduction stage for retrieving the suitable robot actions based on the force perceptions. Computational and experimental results show that the robot is able to learn to pour drinks using the proposed framework, outperforming other approaches such as the classical hidden Markov models in that it requires less training, yields more compact encodings and shows better generalization capabilities.
DescriptionPresentado al 9th RoMoCo celebrado en Kuslin (Polonia) del 3 al 5 de julio de 2013.
Publisher version (URL)http://dx.doi.org/10.1109/RoMoCo.2013.6614613
Identifiersdoi: 10.1109/RoMoCo.2013.6614613
isbn: 978-1-4673-5510-0
Appears in Collections:(IRII) Libros y partes de libros
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