# Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues

arXiv: https://arxiv.org/abs/2404.01986 · HTML: https://mlsanity.com/paper/2404.01986/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Multi-task Self-Supervised Visual Learning](https://mlsanity.com/paper/1708.07860/) (2017)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [Appearance-Based Gaze Estimation in the Wild](https://mlsanity.com/paper/1504.02863/) (2015)
- [It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1611.08860/) (2016)
- [GazeOnce: Real-Time Multi-Person Gaze Estimation](https://mlsanity.com/paper/2204.09480/) (2022)
- [Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective](https://mlsanity.com/paper/2304.11293/) (2023)
- [Are you still with me? Continuous Engagement Assessment from a Robot's Point of View](https://mlsanity.com/paper/2001.03515/) (2020)
- [Intention estimation from gaze and motion features for human-robot shared-control object manipulation](https://mlsanity.com/paper/2208.08688/) (2022)
- [Gaze-based intention estimation: principles, methodologies, and applications in HRI](https://mlsanity.com/paper/2302.04530/) (2023)
- [Self-Supervised Prediction of the Intention to Interact with a Service Robot](https://mlsanity.com/paper/2309.07477/) (2023)
- [Starting engagement detection towards a companion robot using multimodal features](https://mlsanity.com/paper/1503.03732/) (2015)

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

- [GazeHTA: End-to-end Gaze Target Detection with Head-Target Association](https://mlsanity.com/paper/2404.10718/) (2024)
- [MINT-RVAE: Multi-Cues Intention Prediction of Human-Robot Interaction using Human Pose and Emotion Information from RGB-only Camera Data](https://mlsanity.com/paper/2509.22573/) (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)
- [Sixth-Sense: Self-Supervised Learning of Spatial Awareness of Humans from a Planar Lidar](https://mlsanity.com/paper/2502.21029/) (2025)
- [Multi-scale Object-Aware Gaze Estimation via Geometric Reasoning](https://mlsanity.com/paper/2606.29334/) (2026)

## Abstract (arXiv)

For a service robot, it is crucial to perceive as early as possible that an approaching person intends to interact: in this case, it can proactively enact friendly behaviors that lead to an improved user experience. We solve this perception task with a sequence-to-sequence classifier of a potential user intention to interact, which can be trained in a self-supervised way. Our main contribution is a study of the benefit of features representing the person's gaze in this context. Extensive experiments on a novel dataset show that the inclusion of gaze cues significantly improves the classifier performance (AUROC increases from 84.5% to 91.2%); the distance at which an accurate classification can be achieved improves from 2.4 m to 3.2 m. We also quantify the system's ability to adapt to new environments without external supervision. Qualitative experiments show practical applications with a waiter robot.