Name
Title | ||
|---|---|---|
Periodic motion control by modulating CPG parameters based on time-series recognition |
Abstract | ||
|---|---|---|
This paper proposes a computational motion control model of a redundant manipulator inspired by biological brain-motor systems. The proposed model consists of two processing layers dubbed “CPG” and “Dynamical memory”. Likewise biological central pattern generators in spinal cord, the CPG layer plays a role in generating torque patterns for realizing periodic motions. On the contrary, the higher brain model, i.e. the Dynamical memory layer is a time-series pattern discriminator implemented by a recurrent neural networks (RNN). By associating time-series of the system states with optimized CPG parameters, the RNN can predictively modulate the generating torque patterns by recalling well-suited CPG parameters according to the sensorimotor time-series. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11553090_91 | ECAL |
Keywords | Field | DocType |
dynamical memory layer,modulating cpg parameter,well-suited cpg parameter,time-series recognition,cpg layer,time-series pattern discriminator,sensorimotor time-series,computational motion control model,optimized cpg parameter,periodic motion control,higher brain model,dynamical memory,motor system,central pattern generator,time series,motion control,recurrent neural network | Periodic function,Motion control,Discriminator,Computer science,CpG site,Digital pattern generator,Recurrent neural network,Artificial intelligence,Central pattern generator,Artificial neural network | Conference |
Volume | ISSN | ISBN |
3630 | 0302-9743 | 3-540-28848-1 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toshiyuki Kondo | 1 | 131 | 28.57 |
| Koji Ito | 2 | 24 | 7.23 |