  Recently, collaborative robots have begun to train humans to achieve complex
tasks, and the mutual information exchange between them can lead to successful
robot-human collaborations. In this paper we demonstrate the application and
effectiveness of a new approach called mutual reinforcement learning (MRL),
where both humans and autonomous agents act as reinforcement learners in a
skill transfer scenario over continuous communication and feedback. An
autonomous agent initially acts as an instructor who can teach a novice human
participant complex skills using the MRL strategy. While teaching skills in a
physical (block-building) ($n=34$) or simulated (Tetris) environment ($n=31$),
the expert tries to identify appropriate reward channels preferred by each
individual and adapts itself accordingly using an exploration-exploitation
strategy. These reward channel preferences can identify important behaviors of
the human participants, because they may well exercise the same behaviors in
similar situations later. In this way, skill transfer takes place between an
expert system and a novice human operator. We divided the subject population
into three groups and observed the skill transfer phenomenon, analyzing it with
Simpson"s psychometric model. 5-point Likert scales were also used to identify
the cognitive models of the human participants. We obtained a shared cognitive
model which not only improves human cognition but enhances the robot's
cognitive strategy to understand the mental model of its human partners while
building a successful robot-human collaborative framework.
