Toward Natural Human-Robot Collaboration
As robots become integrated into human environments, they increasingly interact directly with people. This is particularly true for assistive robots, which help people through social interactions (like tutoring) or physical interactions (like preparing a meal). Developing effective human-robot interactions in these cases requires a multidisciplinary approach involving both fundamental algorithms from robotics and insights from cognitive science. My research brings together these two areas to extend the science of human-robot interaction, with a particular focus on assistive robotics. In the process of developing cognitively-inspired algorithms for robot behavior, I seek to answer fundamental questions about human-robot interaction: what makes a robot appear intelligent? How can robots communicate their internal states to human partners to improve their ability to collaborate? Vice versa, how can robots "read" human behaviors that reveal people's goals, intentions, and difficulties, to identify where assistance is required?
In this talk, I describe my vision for robots that collaborate with humans on complex tasks by leveraging natural, intuitive human behaviors. I explain how models of human attention, drawn from cognitive science, can help select robot behaviors that improve human performance on a collaborative task. I detail my work on algorithms that predict people's mental states based on their eye gaze and provide assistance in response to those predictions. And I show how breaking the seamlessness of an interaction can make robots appear smarter. Throughout the talk, I will describe how techniques and knowledge from cognitive science help us develop robot algorithms that lead to more effective interactions between people and their robot partners.