Affordance-Based Human–Robot Interaction With Reinforcement Learning

Object-object interaction affordance learning

This paper presents a novel object–object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human–object–objectway. The innate interaction-affordance knowledge of the paired objects...

Affordance-Based Human-Robot Interaction

In our targeted scenario, humans can flexibly establish joint object reference with a robot entirely on the basis of their own intuitions. To reach this aim, the robot needs to be equipped with the kind of knowledge that can be matched in a cognitively adequate way to users’ intuitive conceptual and linguistic preferences. Such an endeavour requires knowledge about human spatial object referenc...

Learning Interactive Affordance for Human-Robot Interaction

In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to ...

Learning Social Affordance for Human-Robot Interaction

In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to ...

Representing and learning affordance-based behaviors