Probabilistic Gaze Imitation and Saliency Learning in a Robotic Head

— Imitation is a powerful mechanism for transferring knowledge from an instructor to a na¨ıve observer, one that is deeply contingent on a state of shared attention between these two agents. In this paper we present Bayesian algorithms that implement the core of an imitation learning framework. We use gaze imitation, coupled with task-dependent saliency learning, to build a state of shared attention between the instructor and observer. We demonstrate the performance of our algorithms in a gaze following and saliency learning task implemented on an active vision robotic head. Our results suggest that the ability to follow gaze and learn instructor- and task-specific saliency models could play a crucial role in building systems capable of complex forms of human-robot interaction. I. IMITATION LEARNING AND SHARED ATTENTION Imitation is a powerful mechanism for transferring knowledge from a skilled agent (the instructor) to an unskilled agent (or observer) using direct manipulation of...

• Type: Conference
• Year: 2005
• Where: ICRA
• Authors: Aaron P. Shon, David B. Grimes, Chris Baker, Matthew W. Hoffman, Shengli Zhou, Rajesh P. N. Rao
• Keywords: Gaze Imitation, ICRA 2005, Imitation Learning Framework, Robotics, Shared Attention