Human gaze is known to be a strong indicator of underlying human intentions and goals during manipulation tasks. This work studies gaze patterns of human teachers demonstrating tasks to robots and proposes ways in which such patterns can be used to enhance robot learning. Using both kinesthetic teaching and video demonstrations, we identify novel intention-revealing gaze behaviors during teaching. These prove to be informative in a variety of problems ranging from reference frame inference to segmentation of multi-step tasks. Based on our findings, we propose two proof-of-concept algorithms which show that gaze data can enhance subtask classification for a multi-step task up to 6 and reward inference and policy learning for a single-step task up to 67 . Our findings provide a foundation for a model of natural human gaze in robot learning from demonstration settings and present open problems for utilizing human gaze to enhance robot learning.
There is also a rich body of work on eye gaze for human-robot interaction @cite_9 . use nonverbal cues including gaze to study timing coordination between humans and robots. Gaze information has also been shown to enable the establishment of joint attention between the human and robot partner, the recognition of human behavior and the execution of anticipatory actions @cite_9 . However, these prior works focus on gaze cues generated by the robot and not on gaze cues from humans. More recently, studied human gaze behavior for shared manipulation, where users controlled a robot arm mounted on a wheelchair via a joystick for assistive tasks of daily living. Novel patterns of gaze behaviors were identified, such as people using visual feedback for aligning the robot arm in a certain orientation and cognitive load being higher for teleoperation versus the shared autonomy condition. However, eye gaze behavior of human teachers has not been studied in the context of robot learning from demonstrations.
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This article reviews the state of the art in social eye gaze for human-robot interaction (HRI). It establishes three categories of gaze research in HRI, defined by differences in goals and methods: a human-centered approach, which focuses on people's responses to gaze; a design-centered approach, which addresses the features of robot gaze behavior and appearance that improve interaction; and a technology-centered approach, which is concentrated on the computational tools for implementing social eye gaze in robots. This paper begins with background information about gaze research in HRI and ends with a set of open questions.