Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach

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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.