Name
Abstract | ||
|---|---|---|
Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the of tenused tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TRO.2010.2069990 | IEEE Transactions on Robotics |
Keywords | Field | DocType |
Robot sensing systems,Neurocontrollers,Finite impulse response filter,Computational complexity,Adaptation model,Least squares approximation | Neurorobotics,Control theory,Robustness (computer science),Control engineering,Adaptive filter,Adaptive control,Sensory system,Mobile robot,Mathematics,Internal model,Whisking in animals | Journal |
Volume | Issue | ISSN |
26 | 6 | 1552-3098 |
Citations | PageRank | References |
9 | 0.98 | 15 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sean R. Anderson | 1 | 89 | 14.87 |
| Martin J. Pearson | 2 | 215 | 26.34 |
| Anthony Pipe | 3 | 41 | 3.95 |
| Prescott, T.J. | 4 | 90 | 9.14 |
| Paul Dean | 5 | 93 | 10.90 |
| John Porrill | 6 | 352 | 85.11 |