Understanding Teacher Gaze Patterns for Robot Learning
It addresses the problem of improving robot learning efficiency from human demonstrations for robotics researchers, though it is incremental as it builds on existing gaze and learning-from-demonstration methods.
This work studied human gaze patterns during robot teaching demonstrations, identifying intention-revealing behaviors that improve tasks like reference frame inference and task segmentation. The proposed algorithms enhanced subtask classification by up to 6% and reward inference and policy learning by up to 67%.
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.