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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Minqi Gao Shi, B.E. |
| Copyright Year | 2009 |
| Description | Author affiliation: Hong Kong University of Science and Technology, Hong Kong (Minqi Gao; Shi, B.E.) |
| Abstract | Image motion due to self motion is an important cue biological systems use for gathering information about the environment. The motion energy model is commonly used to model the responses of motion selective neurons in the mammalian primary visual cortex. Here, we investigate the hypothesis that these low level responses are directly useful for navigation. This avoids the need for estimating a model of the environment and the delay incurred in computing it. In order to discover the relationship between the neuron responses and the motor control required to avoid obstacles in the environment, we use reinforcement learning to train a robot equipped with infrared depth sensors to avoid objects using the outputs of simulated motion energy neurons by minimizing the long term average of the infrared signals it receives. Our experiments with a Koala robot indicate that the motion energy neuron outputs can effectively trigger obstacle avoidance motions in advance of those triggered by the infrared sensors. |
| Starting Page | 994 |
| Ending Page | 999 |
| File Size | 642595 |
| Page Count | 6 |
| File Format | |
| ISBN | 9788995605622 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-08-27 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Subject Keyword | Biological system modeling Robot control Neurons Delay estimation Biological systems Infrared sensors Brain modeling Robot sensing systems Mobile robots Motion control |
| Content Type | Text |
| Resource Type | Article |
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