Human skill transfer: neural networks as learners and teachers

Much work in recent years has focused on transferring human skill to robots by abstracting that skill into a machine-understandable, computational model. Such skill models, however, can be used not only for transferring human control strategy to robots, but also for helping less-skilled human operators improve their performance. We propose a two-step approach for transferring skill from human expert to human apprentice. An expert’s relevant control strategies or skills are first abstracted into a sensory-based computational model. Afterwards, this trained computational model is used to generate on-line advice for lessskilled operators who need to improve their skill. This advice can take advantage of many different sensor modalities, thereby potentially improving both the quality and speed of learning for the apprentice. Furthermore, this approach allows for the efficient transfer of skill from a single expert to many apprentices, as well as from many experts to a single apprentice. In this paper, we first describe a flexible neural-network-based method for modeling human control strategy and provide motivation for its use. We then present a case study for teaching control strategy from one person to another in this two-step approach of transferring skill.