# GazeVLA: Learning Human Intention for Robotic Manipulation

arXiv: https://arxiv.org/abs/2604.22615 · HTML: https://mlsanity.com/paper/2604.22615/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2026.

## Built on by (most cited first, 9 of 9)

- [Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation](https://mlsanity.com/paper/2605.30282/) (2026)
- [IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation](https://mlsanity.com/paper/2605.14712/) (2026)
- [Robot Learning from Human Videos: A Survey](https://mlsanity.com/paper/2604.27621/) (2026)
- [mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar](https://mlsanity.com/paper/2609.34220/) (2026)
- [EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration](https://mlsanity.com/paper/2607.19745/) (2026)
- [PhotoHOI: Synthesizing 3D Hand-Object Interactions from a Single RGB Photograph](https://mlsanity.com/paper/2608.01905/) (2026)
- [Act with Intent: Distilling Behavior Intent for Vision-Language-Action Models](https://mlsanity.com/paper/2608.23478/) (2026)
- [ActGaze: Learning Action-Grounded Gaze through Counterfactual Visual Interventions for High-Precision Manipulation](https://mlsanity.com/paper/2609.28955/) (2026)
- [EyeRobot 2.0: Active Gaze for Precise Manipulation without Wrist Cameras](https://mlsanity.com/paper/2610.03710/) (2026)

## Abstract (arXiv)

Embodied foundation models have achieved significant breakthroughs in robotic manipulation, yet they still depend heavily on large-scale robot demonstrations. Although recent works have explored leveraging human data to alleviate this dependency, effectively extracting transferable knowledge remains a significant challenge due to the inherent embodiment gap between human and robot. We argue that the intention underlying human actions can serve as a powerful intermediate representation for bridging this gap. In this paper, we introduce a novel framework that explicitly learns and transfers human intention to facilitate robotic manipulation. Specifically, we model intention through gaze, as it naturally precedes physical actions and serves as an observable proxy for human intent. Our model is first pretrained on a large-scale egocentric human dataset to capture human intention and its synergy with action, followed by finetuning on a small set of robot and human data. During inference, the model adopts a Chain-of-Thought reasoning paradigm, sequentially predicting intention before executing the action. Extensive evaluations in simulation and real-world settings, across long-horizon and fine-grained tasks, and under few-shot and robustness benchmarks, show that our method consistently outperforms strong baselines, generalizes better, and achieves state-of-the-art performance. Project page: https://gazevla.github.io .