Humanoid Tactile Gesture Production using a Hierarchical SOM-based Encoding
Autonomous Mental Development, IEEE Transactions (2014)
摘要
The existence of cortical hierarchies has long since been established and the advantages of hierarchical encoding of sensor-motor data for control, have long been recognized. Less well understood are the developmental processes whereby such hierarchies are constructed and subsequently used. This paper presents a new algorithm for encoding sequential sensor and actuator data in a dynamic, hierarchical neural network that can grow to accommodate the length of the observed interactions. The algorithm uses a developmental robotics methodology as it extends the Constructivist Learning Architecture, a computational theory of infant cognitive development. This paper presents experimental data demonstrating how the extended algorithm goes beyond the original theory by supporting goal oriented control. The domain studied is the encoding and reproduction of tactile gestures in humanoid robots. In particular, we present results from using a Programming by Demonstration approach to encode a stroke gesture. Our results demonstrate how the novel encoding enables a Nao humanoid robot with a touch sensitive fingertip to successfully encode and reproduce a stroke gesture in the presence of perturbations from internal and external forces.
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关键词
automatic programming,control engineering computing,gesture recognition,humanoid robots,neurocontrollers,self-organising feature maps,Kohonen self-organizing map,Nao humanoid robot,computational theory,constructivist learning architecture,developmental robotics methodology,goal oriented control,hierarchical SOM-based encoding,hierarchical neural network,humanoid tactile gesture production,infant cognitive development,programming by demonstration,stroke gesture,tactile gestures encoding,tactile gestures reproduction,touch sensitive fingertip,Artificial neural networks,programming by demonstration,tactile gestures
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