Opportunities for Enhanced Robot Control along the Adjustable Autonomy Scale

Abstract

As we move along the scale of adjustable autonomy for the control of robots from direct teleoperation at one extreme to full automation at the other, several opportunities for improvement in control quality, user feedback and machine learning suggest themselves. We describe three experiments, in telerobotics, the provision of situational awareness, and the acquisition of knowledge for automation from the human operator, and explain our concept of explicit, assigned responsibility as an organising principle for flexible work-sharing between humans and robots. A novel design for an interface based on this principle is outlined.