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

AutorRozo, Leonel; Jiménez Schlegl, Pablo; Torras, Carme
Fecha de publicación2013
EditorInstitute of Electrical and Electronics Engineers
Citación9th Workshop on Robot Motion and Control: 227-232 (2013)
ResumenRobot 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.
DescripciónPresentado al 9th RoMoCo celebrado en Kuslin (Polonia) del 3 al 5 de julio de 2013.
Versión del editorhttp://dx.doi.org/10.1109/RoMoCo.2013.6614613
Identificadoresdoi: 10.1109/RoMoCo.2013.6614613
isbn: 978-1-4673-5510-0
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