Programming robots by example

摘要

This article presents a prototypical machine learning system (ETAR) that acquir programs for robot tasks. the long-term goal of this project is to discover how to make computer technology, in particular robots, more useful to (and controllable by) people in general. Rather than require programming expertise, the idea is to build a system that learns robot programs from users' examples. Thus the ETAR learning algorithm begins by sampling the robot path while a user physically leads it through the task. A general procedure, possibly containing loops, branches, and variables, is induced from these examples. the ETAR algorithm is novel because an implicit focus mechaism is used to control the entire generalization process. the focus forces ETAR to concentrate on the important domain objects, thus eliminating useless steps and translat the sampled sequence into a series of robot primitive motions. Loops and branches are introduced as the focus objects repeat or differ. Finally, robot positional variables are introduced as functions of the common characteristics of the objects in the focus. the programs that ETAR generates for three tasks-block stacking, obtaining an object with a certain characteristic, and sorting-are shown to provide an intuitive feel for the types of tasks that ETAR can learn. the article concludes with a general discussion of the current issues in programming by example and describes how this new learner is related to previous systems in this area. ETAR has been implemented on an Excalibur robot © 1993 John Wiley & Sons, Inc.