# Gaze-Based Intention Recognition for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2405.07570 · HTML: https://mlsanity.com/paper/2405.07570/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2024.

## Built on by (most cited first, 5 of 5)

- [A Comparative Study of Human Activity Recognition: Motion, Tactile, and multi-modal Approaches](https://mlsanity.com/paper/2505.08657/) (2025)
- [Real-Time Human-Robot Interaction Intent Detection Using RGB-based Pose and Emotion Cues with Cross-Camera Model Generalization](https://mlsanity.com/paper/2512.17958/) (2025)
- [A Social Robot with Inner Speech for Dietary Guidance](https://mlsanity.com/paper/2505.08664/) (2025)
- [Seeing My Future: Predicting Situated Interaction Behavior in Virtual Reality](https://mlsanity.com/paper/2510.10742/) (2025)
- [A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration](https://mlsanity.com/paper/2602.07024/) (2026)

## Abstract (arXiv)

This work aims to tackle the intent recognition problem in Human-Robot Collaborative assembly scenarios. Precisely, we consider an interactive assembly of a wooden stool where the robot fetches the pieces in the correct order and the human builds the parts following the instruction manual. The intent recognition is limited to the idle state estimation and it is needed to ensure a better synchronization between the two agents. We carried out a comparison between two distinct solutions involving wearable sensors and eye tracking integrated into the perception pipeline of a flexible planning architecture based on Hierarchical Task Networks. At runtime, the wearable sensing module exploits the raw measurements from four 9-axis Inertial Measurement Units positioned on the wrists and hands of the user as an input for a Long Short-Term Memory Network. On the other hand, the eye tracking relies on a Head Mounted Display and Unreal Engine. We tested the effectiveness of the two approaches with 10 participants, each of whom explored both options in alternate order. We collected explicit metrics about the attractiveness and efficiency of the two techniques through User Experience Questionnaires as well as implicit criteria regarding the classification time and the overall assembly time. The results of our work show that the two methods can reach comparable performances both in terms of effectiveness and user preference. Future development could aim at joining the two approaches two allow the recognition of more complex activities and to anticipate the user actions.