HCBPM : An Idea toward a Social Learning Environment for Humanoid Robot

Abstract EN

To advance robotics toward real-world applications, a growing body of research has focused on the development of control systems for humanoid robots in recent years.

Several approaches have been proposed to support the learning stage of such controllers, where the robot can learn new behaviors by observing and/or receiving direct guidance from a human or even another robot.

These approaches require dynamic learning and memorization techniques, which the robot can use to reform and update its internal systems continuously while learning new behaviors.

Against this background, this study investigates a new approach to the development of an incremental learning and memorization model.

This approach was inspired by the principles of neuroscience, and the developed model was named “Hierarchical Constructive Backpropagation with Memory” (HCBPM).

The validity of the model was tested by teaching a humanoid robot to recognize a group of objects through natural interaction.

The experimental results indicate that the proposed model efficiently enhances real-time machine learning in general and can be used to establish an environment suitable for social learning between the robot and the user in particular.