Gaze-based, context-aware robotic system for assisted reaching and grasping

Abstract

Assistive robotic systems endeavour to support those with movement disabilities, enabling them to move againand regain functionality. Main issue with these systems is the complexity of their low-level control, and how to translate thisto simpler, higher level commands that are easy and intuitivefor a human user to interact with. We have created a multi-modal system, consisting of different sensing, decision makingand actuating modalities, to create intuitive, human-in-the-loopassistive robotics. The system takes its cue from the user’s gaze,to decode their intentions and implement lower-level motionactions and achieve higher level tasks. This results in the usersimply having to look at the objects of interest, for the robotic system to assist them in reaching for those objects, grasping them, and using them to interact with other objects. We presentour method for 3D gaze estimation, and action grammars-basedimplementation of sequences of action through the robotic system. The 3D gaze estimation is evaluated with 8 subjects,showing an overall accuracy of 4.68±0.14cm. The full systemis tested with 5 subjects, showing successful implementation of 100% of reach to gaze point actions and full implementationof pick and place tasks in 96%, and pick and pour tasks in76% of cases. Finally we present a discussion on our results and what future work is needed to improve the system.