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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Wu Demiris, Y. |
| Copyright Year | 2010 |
| Description | Author affiliation: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom (Yan Wu; Demiris, Y.) |
| Abstract | 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. |
| Starting Page | 453 |
| Ending Page | 458 |
| File Size | 996823 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424478149 |
| e-ISBN | 9781424478156 |
| DOI | 10.1109/ICARCV.2010.5707349 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-12-07 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robots Humans Games Correlation Cameras Redundancy Trajectory humanoid robots imitation learning one-shot learning generative model path planning |
| Content Type | Text |
| Resource Type | Article |
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