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Hierarchical learning approach for one-shot action imitation in humanoid robots

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2 Author(s)
Yan Wu ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Demiris, Y.

We consider the issue of segmenting an action in the learning phase into a logical set of smaller primitives in order to construct a generative model for imitation learning using a hierarchical approach. Our proposed framework, addressing the “how-to” question in imitation, is based on a one-shot imitation learning algorithm. It incorporates segmentation of a demonstrated template into a series of subactions and takes a hierarchical approach to generate the task action by using a finite state machine in a generative way. Two sets of experiments have been conducted to evaluate the performance of the framework, both statistically and in practice, through playing a tic-tac-toe game. The experiments demonstrate that the proposed framework can effectively improve the performance of the one-shot learning algorithm and reduce the size of primitive space, without compromising the learning quality.

Published in:

Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on

Date of Conference:

7-10 Dec. 2010