DocumentCode :
2775940
Title :
S-Learning: A Biomimetic Algorithm for Learning, Memory, and Control in Robots
Author :
Rohrer, Brandon
Author_Institution :
Intelligent Syst., Robotics, & Cybern. Group, Sandia Nat. Labs., Albuquerque, NM
fYear :
2007
fDate :
2-5 May 2007
Firstpage :
148
Lastpage :
151
Abstract :
S-learning is a sequence-based learning algorithm patterned on human motor behavior. Discrete-time and quantized sensory information is amassed in real-time to form a dynamic model of the system being controlled and its environment. No explicit model is provided a priori, nor any hint about what the structure of the model might be. As the core of a Brain-Emulating Cognition and Control Architecture (BECCA), S-Learning provides a mechanism for human-inspired learning, memory, and control in machines. In a simulation of a point-to-point reaching task, S-Learning demonstrates several attributes of human motor behavior, including learning through exploration and task transfer.
Keywords :
biomimetics; brain models; cognition; intelligent control; learning (artificial intelligence); robots; S-learning; biomimetic algorithm; brain-emulating cognition and control architecture; human motor behavior; human-inspired learning; point-to-point reaching task; quantized sensory information; robots; sequence-based learning algorithm; Biomimetics; Brain modeling; Cognition; Computer architecture; Humans; Intelligent robots; Libraries; Machine learning; Robot control; Sparse matrices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Engineering, 2007. CNE '07. 3rd International IEEE/EMBS Conference on
Conference_Location :
Kohala Coast, HI
Print_ISBN :
1-4244-0792-3
Electronic_ISBN :
1-4244-0792-3
Type :
conf
DOI :
10.1109/CNE.2007.369634
Filename :
4227239
Link To Document :
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