Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping

TL;DR

This paper introduces a gaze-based, context-aware assistive robotic system that interprets user gaze to perform reaching, grasping, and interaction tasks, simplifying control for users with movement disabilities.

Contribution

The authors developed a multi-modal, human-in-the-loop robotic system utilizing gaze estimation and grammars-based action sequences for intuitive assistance.

Findings

Gaze estimation accuracy of 4.68cm

100% success in reach-to-gaze actions

96% success in pick-and-place tasks

Abstract

Assistive robotic systems endeavour to support those with movement disabilities, enabling them to move again and regain functionality. Main issue with these systems is the complexity of their low-level control, and how to translate this to simpler, higher level commands that are easy and intuitive for a human user to interact with. We have created a multi-modal system, consisting of different sensing, decision making and actuating modalities, leading to intuitive, human-in-the-loop assistive robotics. The system takes its cue from the user's gaze, to decode their intentions and implement low-level motion actions to achieve high-level tasks. This results in the user simply 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 present our method for 3D gaze estimation, and grammars-based implementation of sequences of action with the robotic system. The 3D gaze estimation is evaluated with 8 subjects, showing an overall accuracy of $4.68\pm0.14cm$. The full system is tested with 5 subjects, showing successful implementation of $100\%$ of reach to gaze point actions and full implementation of pick and place tasks in 96\%, and pick and pour tasks in $76\%$ of cases. Finally we present a discussion on our results and what future work is needed to improve the system.