# Gaze-based intention estimation: principles, methodologies, and applications in HRI

arXiv: https://arxiv.org/abs/2302.04530 · HTML: https://mlsanity.com/paper/2302.04530/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 154 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming](https://mlsanity.com/paper/1706.00155/) (2017)
- [Action Anticipation: Reading the Intentions of Humans and Robots](https://mlsanity.com/paper/1802.02788/) (2018)
- [Gaze-based, Context-aware Robotic System for Assisted Reaching and Grasping](https://mlsanity.com/paper/1809.08095/) (2018)
- [Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction](https://mlsanity.com/paper/1802.10503/) (2018)
- [Intention estimation from gaze and motion features for human-robot shared-control object manipulation](https://mlsanity.com/paper/2208.08688/) (2022)
- [Behavioral Research and Practical Models of Drivers' Attention](https://mlsanity.com/paper/2104.05677/) (2021)
- [A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision](https://mlsanity.com/paper/1908.10933/) (2019)
- [Advanced statistical methods for eye movement analysis and modeling: a gentle introduction](https://mlsanity.com/paper/1506.07194/) (2015)
- [Learning Arbitration for Shared Autonomy by Hindsight Data Aggregation](https://mlsanity.com/paper/1906.12280/) (2019)

## Built on by (most cited first, 11 of 11)

- [Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers](https://mlsanity.com/paper/2507.15833/) (2025)
- [UniGaze: Towards Universal Gaze Estimation via Large-scale Pre-Training](https://mlsanity.com/paper/2502.02307/) (2025)
- [Predicting the Intention to Interact with a Service Robot:the Role of Gaze Cues](https://mlsanity.com/paper/2404.01986/) (2024)
- [Gaze-Guided Learning: Avoiding Shortcut Bias in Visual Classification](https://mlsanity.com/paper/2504.05583/) (2025)
- [ARGaze: Autoregressive Transformers for Online Egocentric Gaze Estimation](https://mlsanity.com/paper/2602.05132/) (2026)
- [Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2306.11483/) (2023)
- [Seeing My Future: Predicting Situated Interaction Behavior in Virtual Reality](https://mlsanity.com/paper/2510.10742/) (2025)
- [Int3DNet: Scene-Motion Cross Attention Network for 3D Intention Prediction in Mixed Reality](https://mlsanity.com/paper/2603.13355/) (2026)
- [GazeOnce360: Fisheye-Based 360deg Multi-Person Gaze Estimation with Global-Local Feature Fusion](https://mlsanity.com/paper/2603.17161/) (2026)
- [How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation](https://mlsanity.com/paper/2607.01437/) (2026)
- [Automatic Field-of-View Adjustment for a View-Expansive Microscope via LSTM-Based Gaze and Pipette Motion Interpretation](https://mlsanity.com/paper/2608.10401/) (2026)

## Abstract (arXiv)

Intention prediction has become a relevant field of research in Human-Machine and Human-Robot Interaction. Indeed, any artificial system (co)-operating with and along humans, designed to assist and coordinate its actions with a human partner, would benefit from first inferring the human's current intention. To spare the user the cognitive burden of explicitly uttering their goals, this inference relies mostly on behavioral cues deemed indicative of the current action. It has been long known that eye movements are highly anticipatory of the single steps unfolding during a task, hence they can serve as a very early and reliable behavioural cue for intention recognition. This review aims to draw a line between insights in the psychological literature on visuomotor control and relevant applications of gaze-based intention recognition in technical domains, with a focus on teleoperated and assistive robotic systems. Starting from the cognitive principles underlying the relationship between intentions, eye movements, and action, the use of eye tracking and gaze-based models for intent recognition in Human-Robot Interaction is considered, with prevalent methodologies and their diverse applications. Finally, special consideration is given to relevant human factors issues and current limitations to be factored in when designing such systems.