What You See is What You Grasp: User-Friendly Grasping Guided by Near-eye-tracking

TL;DR

This paper introduces a human-robot interface that uses near-eye-tracking to infer user intentions and guide robotic grasping, enabling intuitive sight-based manipulation for assistive applications.

Contribution

It presents a novel system combining near-eye-tracking with a transformer-based grasp model for sight-guided robotic manipulation.

Findings

Low gaze estimation error achieved

Promising grasping results on multiple datasets

Effective integration of eye-tracking with robotic control

Abstract

This work presents a next-generation human-robot interface that can infer and realize the user's manipulation intention via sight only. Specifically, we develop a system that integrates near-eye-tracking and robotic manipulation to enable user-specified actions (e.g., grasp, pick-and-place, etc), where visual information is merged with human attention to create a mapping for desired robot actions. To enable sight guided manipulation, a head-mounted near-eye-tracking device is developed to track the eyeball movements in real-time, so that the user's visual attention can be identified. To improve the grasping performance, a transformer based grasp model is then developed. Stacked transformer blocks are used to extract hierarchical features where the volumes of channels are expanded at each stage while squeezing the resolution of feature maps. Experimental validation demonstrates that the eye-tracking system yields low gaze estimation error and the grasping system yields promising results on multiple grasping datasets. This work is a proof of concept for gaze interaction-based assistive robot, which holds great promise to help the elder or upper limb disabilities in their daily lives. A demo video is available at https://www.youtube.com/watch?v=yuZ1hukYUrM