# Gaze Estimation for Human-Robot Interaction: Analysis Using the NICO Platform

arXiv: https://arxiv.org/abs/2509.24001 · HTML: https://mlsanity.com/paper/2509.24001/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [Gaze360: Physically Unconstrained Gaze Estimation in the Wild](https://mlsanity.com/paper/1910.10088/) (2019)
- [Eye Tracking for Everyone](https://mlsanity.com/paper/1606.05814/) (2016)
- [ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation](https://mlsanity.com/paper/2007.15837/) (2020)
- [MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation](https://mlsanity.com/paper/1711.09017/) (2017)
- [HeadGAN: One-shot Neural Head Synthesis and Editing](https://mlsanity.com/paper/2012.08261/) (2020)
- [Gaze Estimation using Transformer](https://mlsanity.com/paper/2105.14424/) (2021)
- [L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments](https://mlsanity.com/paper/2203.03339/) (2022)
- [Deep Pictorial Gaze Estimation](https://mlsanity.com/paper/1807.10002/) (2018)
- [Appearance-Based Gaze Estimation Using Dilated-Convolutions](https://mlsanity.com/paper/1903.07296/) (2019)
- [Towards End-to-end Video-based Eye-Tracking](https://mlsanity.com/paper/2007.13120/) (2020)
- [Weakly-Supervised Physically Unconstrained Gaze Estimation](https://mlsanity.com/paper/2105.09803/) (2021)
- [Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues](https://mlsanity.com/paper/1805.03064/) (2018)
- [3DGazeNet: Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views](https://mlsanity.com/paper/2212.02997/) (2022)
- [Enhancing 3D Gaze Estimation in the Wild using Weak Supervision with Gaze Following Labels](https://mlsanity.com/paper/2502.20249/) (2025)
- [Appearance-based gaze estimation enhanced with synthetic images using deep neural networks](https://mlsanity.com/paper/2311.14175/) (2023)

## Built on by (most cited first, 1 of 1)

- [PAGE: Towards Practical Human-level Gaze Target Estimation](https://mlsanity.com/paper/2607.04860/) (2026)

## Abstract (arXiv)

This paper evaluates the current gaze estimation methods within a human-robot interaction (HRI) context of a shared workspace scenario. We introduce a new, annotated dataset collected with the NICO robotic platform. We evaluate four state-of-the-art gaze estimation models. The evaluation shows that the angular errors are close to those reported on general-purpose benchmarks. However, when expressed in terms of distance in the shared workspace the best median error is 14.57~cm, quantifying the practical limitations of current methods. We conclude by discussing these limitations and offering recommendations on how to best integrate gaze estimation as a modality in HRI systems.