Amy Larson and Richard Voyles, University of Minnesota
Robotic systems are capable of complex behavior by sequencing simpler skills called primitives. A primitive is a sensor/actuator mapping robust enough to perform appropriately in various situations. Coding both the primitives and the sequencing of primitives can be tedious and requires an accurate translation of human knowledge to machine code. Programming by human demonstration addresses these problems of acquiring and combining primitives. Programming by demonstration can be implemented with a supervised learning technique such as artificial neural networks (ANN). Problems exist with such techniques, however, including creating a training set which is comprehensive and concise. Here, we present a method for nonexpert users to collect "good" training data from an intuitive understanding of task behavior, not from knowledge of the underlying learning mechanism.