Programming Robot Behavior Primitives Through Human Demonstration

Abstract

Robotic systems are capable of complex behavior by se-quencing simpler skills called primitives (Voyles, Morrow, & Khosla 1997). A primitive is a sensor/actuator mapping robust enough to perform appropriately in various situations. Programming one primitive can be tedious and requires an accurate translation of human knowledge to machine code. Once a sufficient set of primitives is coded, the user must write code to sequence the primitives – also tedious and difficult. Programming by human demonstration addresses these problems of acquiring and combining primitives. To create primitives, programming by demonstration can be implemented with a supervised learning technique such as artificial neural networks (ANN) to learn a sen-sor/actuator mapping. Problems exist with such techniques, however, including creating a training set which is compre-