In this paper a hybrid system for motor control on testbeds, consisting of neural networks with a self-organizing process state detection and fuzzy rulebases, is proposed. The basic mechanism used for hybridization is a multiagent system composed from loosely interconnected subsystems for the differ
Neurobiological and neurorobotic approaches to control architectures for a humanoid motor system
β Scribed by Simon F. Giszter; Karen A. Moxon; Ilya A. Rybak; John K. Chapin
- Publisher
- Elsevier Science
- Year
- 2001
- Tongue
- English
- Weight
- 330 KB
- Volume
- 37
- Category
- Article
- ISSN
- 0921-8890
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β¦ Synopsis
In the mammalian nervous system, the sensorimotor interface between cortex and spinal cord plays a key role in novel skill formation and motor learning. We seek to understand the principles of motor learning at this interface using a multidisciplinary approach. We believe this approach will prove relevant to the development of biomimetic control architectures for humanoid robots. Learning at this interface requires an understanding of the spinal output structures. Ultimately, these must form the basis of the algorithms needed for adaptive motor learning. These spinal structures interact with descending cortical control to produce accurate limb trajectories, and novel motor behavior.
π SIMILAR VOLUMES
System developers have found that exploiting parallel architectures for control systems is challenging and often the resulting implementations do not provide the expected performance advantages over traditional uniprocessor solutions. This paper presents a generic method and a suite of design tools