Human-robot Co-learning for fluent collaborations

Abstract

A team develops competency by progressive mutual adaptation and learning, a process we call co-learning. In human teams, partners naturally adapt to each other and learn while collaborating. This is not self-evident in human-robot teams. There is a need for methods and models for describing and enabling co-learning in human-robot partnerships. The presented project aims to study human-robot co-learning as a process that stimulates fluent collaborations. First, it is studied how interactions develop in a context where a human and a robot both have to implicitly adapt to each other and have to learn a task to improve the collaboration and performance. The observed interaction patterns and learning outcomes will be used to (1) investigate how to design learning interactions that support human-robot teams to sustain implicitly learned behavior over time and context, and (2) to develop a mental model of the learning human partner, to investigate whether this supports the robot in its own learning as well as in adapting effectively to the human partner.