1 research outputs found
Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach
This study investigates how adequate coordination among the different
cognitive processes of a humanoid robot can be developed through end-to-end
learning of direct perception of visuomotor stream. We propose a deep dynamic
neural network model built on a dynamic vision network, a motor generation
network, and a higher-level network. The proposed model was designed to process
and to integrate direct perception of dynamic visuomotor patterns in a
hierarchical model characterized by different spatial and temporal constraints
imposed on each level. We conducted synthetic robotic experiments in which a
robot learned to read human's intention through observing the gestures and then
to generate the corresponding goal-directed actions. Results verify that the
proposed model is able to learn the tutored skills and to generalize them to
novel situations. The model showed synergic coordination of perception, action
and decision making, and it integrated and coordinated a set of cognitive
skills including visual perception, intention reading, attention switching,
working memory, action preparation and execution in a seamless manner. Analysis
reveals that coherent internal representations emerged at each level of the
hierarchy. Higher-level representation reflecting actional intention developed
by means of continuous integration of the lower-level visuo-proprioceptive
stream.Comment: Accepted in the IEEE Transactions on Cognitive and Developmental
Systems (TCDS), 201