How to Train Your Robot?

Abstract

This article explores the concept of inferring intent during human-in-the-loop robot learning for output tracking. The human-response dynamics can affect the precision achieved by the human during human-in-the-loop operation. Preview-based online inversion is a viable technique that allows for stable online inversion of complex linear controlled systems, even those for which stable causal inverses do not exist, such as non-minimum phase systems. The averaged motion can still be affected by the human-response dynamics and can therefore be still different from the user’s intent. Therefore, inferring the human intent is important and necessary in the context of human-robot shared control. The results of applying the inversion-based iterative learning scheme to the human-in-the-loop trajectory tracking task has also been presented in the article. Figures show that the output tracking performance improves with respect to the manual tracking performance when inverse control is applied. When the iterative learning control law is applied, further tracking improvement is achieved. Thus, the learned control input can successfully emulate the human intent. An advantage of the robot-learning framework is that it allows novice human operators, who may be experts in the task, but not in teaching a robot, to successfully achieve the task objectives, which can expand the usage and acceptability of robots in society.