Demiris, Y

Wu, Y

English

16/01/13 meb. pre-print version, OK to pub.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

Wu, Yan

Demiris, Yiannis

Spiral - Imperial College Digital Repository

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Yan Wu

Yiannis Demiris

Crossref

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

http://spiral.imperial.ac.uk/bitstream/10044/1/12666/4/icarcv2010.pdf

Hierarchical Learning Approach for One-shot Action Imitation in Humanoid Robots

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