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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sekiguchi, T. Kobayashi, Y. Shimizu, A. Kaneko, T. |
| Copyright Year | 2012 |
| Description | Author affiliation: Shizuoka University, Japan (Kobayashi, Y.; Kaneko, T.) || Tokyo University of Agriculture and Technology, Japan (Sekiguchi, T.; Shimizu, A.) |
| Abstract | This paper presents an optimal robot motion learning method that involves object manipulation where dynamics of robots and environment are unknown. The dynamics of the environment is acquired by the robot's experience through online learning. A reinforcement learning framework which incorporates model identification is proposed. Based on the learning framework, an idea of effective motion acquisition is proposed through decomposing the task by detecting ‘switching of dynamics’, which is called mode-switching. Object manipulation is divided into two modes, approaching to the object and pushing it toward the goal. This enables the robot to learn motions while reducing number of trials and to behave more dexterously by integrating modes, each of which was learned separately. The proposed learning method is evaluated in simulation of a wheeled robot. It was shown that appropriate motion for re-approaching and re-pushing to accurately move the object to the goal can be realized using the proposed idea of planning with mode switching. |
| Starting Page | 61 |
| Ending Page | 66 |
| File Size | 884000 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467327053 |
| e-ISBN | 9781467327060 |
| DOI | 10.1109/ROSE.2012.6402625 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-11-16 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Switches Approximation methods Mobile robots Trajectory Robot kinematics Optimal control |
| Content Type | Text |
| Resource Type | Article |
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