Learning Strategies for Mid-Level Robot Control: Some Preliminary Considerations and Experiments

Abstract

Versatile robots will need to be programmed, of course. But beyond explicit programming by a programmer, they will need to be able to plan how to perform new tasks and how to perform old tasks under new circumstances. They will also need to be able to learn. In this article, I concentrate on two types of learning, namely supervised learning and reinforcement learning of robot control programs. I argue also that it would be useful for all of these programs, those explicitly programmed, those planned, and those learned, to be expressed in a common language. I propose what I think is a good candidate for such a language, namely the formalism of teleo-reactive (T-R) programs. Most of the article deals with the matter of learning T-R programs. I assert that such programs are PAC learnable and then describe some techniques for learning them and the results of some preliminary learning experiments. The work on learning T-R programs is in a very early stage, but I think enough has been started to warrant further development and experimentation. For that reason I make this article available on the web, but I caution readers about the tentative nature of this work. I solicit comments and suggestions at: