Human robot interaction can boost robot's affordance learning: A proof of concept

Abstract

Affordance, being one of the key building blocks behind how we interact with the environment, is also studied widely in robotics from different perspectives, for navigation, for task planning, etc. Therefore, the study is mostly focused on affordances of individual objects and for robot environment interaction, and such affordances have been mostly perceived through vision and physical interaction. However, in a human centered environment, for a robot to be socially intelligent and exhibit more natural interaction behavior, it should be able to learn affordances also through day-to-day verbal interaction and that too from the perspective of what does the presence of a specific set of objects affords to provide. In this paper, we will present the novel idea of verbal interaction based multi-object affordance learning and a framework to achieve that. Further, an instantiation of the framework on the real robot within office context is analyzed. Some of the potential future works and applications, such as fusing with activity pattern and interaction grounding will be briefly discussed.