AAAI 2017: Learning to Predict Intent From Gaze During Robotic Hand-Eye Coordination

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Effective human-aware robots should anticipate their user’s intentions. During hand-eye coordination tasks, gaze often precedes hand motion and can serve as a powerful predic- tor for intent. However, cooperative tasks where a semi- autonomous robot serves as an extension of the human hand have rarely been studied in the context of hand-eye coordi- nation. We hypothesize that accounting for anticipatory eye movements in addition to the movements of the robot will improve intent estimation. This research compares the appli- cation of various machine learning methods to intent predic- tion from gaze tracking data during robotic hand-eye coor- dination tasks. We found that with proper feature selection, accuracies exceeding 94% and AUC greater than 91% are achievable with several classification algorithms but that an- ticipatory gaze data did not improve intent prediction.

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