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Content Provider | IEEE Xplore Digital Library |
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Author | Hoa-yu Chan Kuu-young Young Hsin-Chia Fu |
Copyright Year | 2010 |
Description | Author affiliation: Department of Computer Science, National Chiao-Tung University, Hsinchu, Taiwan (Hoa-yu Chan; Hsin-Chia Fu) || Department of Electrical Engineering, National Chiao-Tung University, Hsinchu, Taiwan (Kuu-young Young) |
Abstract | When the robot comes to the home-like environment, its programming becomes very demanding. The concept of learning by demonstration is thus introduced, which intends to remove the load of detailed analysis and programming from the user. Following this concept, in this paper, we propose a novel approach for the robot to deduce the intention of the demonstrator from the trajectories during task execution. We focus on the tool-handling task, which is common in the home environment, but complicated for analysis. The proposed approach does not predefine motions or put constraints on motion speed, while allowing the event order to be altered and the presence of redundant operations during demonstration. We apply the concept of cross-validation to locate the portions of the trajectory that correspond to delicate and skillful maneuvering, and apply an algorithm based on dynamic programming previously developed to search for the most probable intention. In experiments, we apply the proposed approach for two different kinds of tasks, the pouring and coffee-making tasks, with the number of objects and their locations varied during demonstrations. |
Starting Page | 459 |
Ending Page | 464 |
File Size | 1189253 |
Page Count | 6 |
File Format | |
ISBN | 9781424476428 |
e-ISBN | 9784907764364 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-08-18 |
Publisher Place | Taiwan |
Access Restriction | Subscribed |
Rights Holder | SICE |
Subject Keyword | Trajectory Humans Manipulators Indexes Green products Training Skill Transfer Intention Learning Human Demonstration Motion Feature Robot Imitation |
Content Type | Text |
Resource Type | Article |
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