A use case of an adaptive cognitive architecture for the operation of humanoid robots in real environments

Future trends in robotics call for robots that can work, interact and collaborate with humans. Developing these kind of robots requires the development of intelligent behaviours. As a minimum standard for behaviours to be considered as intelligent, it is required at least to present the ability to learn skills, represent skill’s knowledge and adapt and generate new skills. In this work, a cognitive framework is proposed for learning and adapting models of robot skills knowledge. The proposed framework is meant to allow for an operator to teach and demonstrate the robot the motion of a task skill it must reproduce; to build a knowledge base of the learned skills knowledge allowing for its storage, classification and retrieval; to adapt and generate new models of a skill for compliance with the current task constraints. This framework has been implemented in the humanoid robot HOAP-3 and experimental results show the applicability of the approach.

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