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Force-based robot learning of pouring skills using parametric hidden markov models.
| Content Provider | CiteSeerX |
|---|---|
| Author | Rozo, Leonel Jiménez, Pablo Torras, Carme |
| Abstract | Abstract — Robot 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. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Pouring Skill Parametric Hidden Markov Model Force-based Robot Learning Haptic Device Force Perception Generalization Capability Task Parameter Training Phase Gaussian Mixture Regression Classical Hidden Markov Model Reproduction Stage Bottle Weight Force-torque Trace Key Role New Challenge Compact Encoding Abstract Robot Experimental Result Suitable Robot Action Certain Force Profile |
| Content Type | Text |
