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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rohrer, B. Bernard, M. Morrow, J.D. Rothganger, F. Xavier, P. |
| Copyright Year | 2009 |
| Abstract | A model-free, biologically-motivated learning and control algorithm called S-learning is described as implemented in an Surveyor SRV-1 mobile robot. S-learning demonstrated learning of robotic and environmental structure sufficient to allow it to achieve its goals (finding high- or low-contrast views in its environment). No modeling information about the task or calibration information about the robot’s actuators and sensors were used in S-learning’s planning. The ability of S-learning to make movement plans was completely dependent on experience it gained as it explored. Initially it had no experience and was forced to wander randomly. With increasing exposure to the task, S-learning achieved its goals with more nearly optimal paths. The fact that this approach is model-free implies that it may be applied to many other systems, perhaps even to systems of much greater complexity. |
| Starting Page | 566 |
| Ending Page | 572 |
| File Size | 1175513 |
| Page Count | 7 |
| File Format | |
| ISBN | 9780769537368 |
| DOI | 10.1109/ICNC.2009.38 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-14 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Actuators model-free biologically-inspired Biological system modeling Computational modeling Laboratories mobile robot Calibration Mobile robots reinforcement learning Learning Biology computing Robot sensing systems sequence learning Biological control systems |
| Content Type | Text |
| Resource Type | Article |
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