Learning to Achieve Different Levels of Adaptability for Human–Robot Collaboration Utilizing a Neuro-Dynamical System

Abstract

Intelligent robots are expected to collaboratively work with humans in dynamically changing daily life environments. To realize successful human-robot collaboration, robots need to deal with latent spatiotemporal complexity in the workspace and the task. To overcome this crucial issue, three levels of adaptability-motion modification, action selection, and role switching-should be considered. This paper demonstrates that a single hierarchically organized neuro-dynamical system called a multiple timescale recurrent neural network can achieve these levels of adaptability by utilizing hierarchical and bidirectional information processing. The system is implemented in a humanoid robot and the robot is required to learn to perform collaborative tasks in which some parts must be performed by a human partner and others by the robot. Experimental results show that the robot can perform collaborative tasks under dynamically changing environments, including both learned and unlearned situations, thanks to different levels of adaptability acquired in the system.