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Task modeling in imitation learning using latent variable models

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4 Author(s)
Carl Henrik Ek ; KTH - Royal Institute of Technology, Stockholm, Sweden ; Dan Song ; Kai Huebner ; Danica Kragic

An important challenge in robotic research is learning and reasoning about different manipulation tasks from scene observations. In this paper we present a probabilistic model capable of modeling several different types of input sources within the same model. Our model is capable to infer the task using only partial observations. Further, our framework allows the robot, given partial knowledge of the scene, to reason about what information streams to acquire in order to disambiguate the state-space the most. We present results for task classification within and also reason about different features discriminative power for different classes of tasks.

Published in:

2010 10th IEEE-RAS International Conference on Humanoid Robots

Date of Conference:

6-8 Dec. 2010