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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Akgun, B. Thomaz, A.L. |
| Copyright Year | 2015 |
| Abstract | We introduce a new method for robots to further improve upon skills acquired through Learning from Demonstration. Previously, we have introduced a method to learn both an action model to execute the skill and a goal model to monitor the execution of the skill. In this paper we show how to use the learned goal models to improve the learned action models autonomously, without further user interaction. Trajectories are sampled from the action model and executed on the robot. The goal model then labels them as success or failure and the successful ones are used to update the action model. We introduce an adaptive sampling method to speed up convergence. We show through both simulation and real robot experiments that our method can fix a failed action model. |
| Starting Page | 5259 |
| Ending Page | 5264 |
| File Size | 1338616 |
| Page Count | 6 |
| File Format | |
| e-ISBN | 9781479999941 |
| DOI | 10.1109/IROS.2015.7354119 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-09-28 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Hidden Markov models Adaptation models Monitoring Robot sensing systems Trajectory Data models |
| Content Type | Text |
| Resource Type | Article |
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