Learning of composite actions and visual categories via grounded linguistic instructions: Humanoid robot simulations

摘要

This paper presents a cognitive learning system for robot recognition and composite action learning. The cognitive system of the robot is an artificial neural network trained to recognize and handle objects through imitation and back-propagation algorithm learning. The robot is first trained to learn the representation of action words, object categories and grounded language understanding. Following a human tutor's linguistic instructions, the robot autonomously transfers the grounding form directly basics knowledge to new higher level composite knowledge.