arxiv: 2603.23202 · v2 · submitted 2026-03-24 · 💻 cs.CV

Recognition: no theorem link

Gaze-Regularized Vision-Language-Action Models for Robotic Manipulation

Anupam Pani , Yanchao Yang

Authors on Pith no claims yet

Pith reviewed 2026-05-15 00:24 UTC · model grok-4.3

classification 💻 cs.CV

keywords vision-language-action modelsgaze regularizationrobotic manipulationattention alignmentKL divergencehuman priorsinductive biastransformer attention

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The pith

Aligning VLA model attention to human gaze patterns via KL divergence improves robotic manipulation success by 4-12%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that human gaze data can serve as an effective training signal for vision-language-action models in robotics. By converting recorded eye movements into patch-level attention targets and adding a KL divergence penalty to the transformer's attention maps, the method steers the model toward task-relevant visual features during learning. This produces higher success rates on standard manipulation benchmarks, faster convergence during training, and greater stability when lighting or sensor inputs vary. The regularization requires no architecture changes and adds no cost at deployment time, allowing it to apply directly to existing datasets and models.

Core claim

The central claim is that transforming temporally aggregated human gaze heatmaps into patch-level probability distributions and regularizing the VLA transformer's attention through KL divergence creates a beneficial inductive bias for fine-grained robotic manipulation tasks, yielding measurable gains in performance, training efficiency, and robustness without modifying the base architecture or inference pipeline.

What carries the argument

The gaze-regularized training objective that converts human gaze heatmaps into patch-level distributions and applies KL divergence to align the model's internal attention maps with those distributions.

If this is right

Success rates on manipulation benchmarks rise by 4-12 percent.
Models reach target performance levels after fewer training steps.
Performance holds up better under changes in lighting and sensor noise.
Learned attention maps become visually interpretable and resemble human strategies.
The method works with standard datasets and adds no runtime overhead.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same regularization could be tested on navigation or grasping tasks where human attention data is already available from existing video corpora.
If gaze priors reduce the volume of robot-specific demonstrations needed, data collection pipelines for new robots could shift toward cheaper human observation recordings.
A direct test would compare gaze-regularized models against models trained with synthetic attention targets to isolate whether the benefit comes from human-specific patterns or simply from any focused attention signal.

Load-bearing premise

Human gaze recordings collected on the same tasks will reliably highlight the visual elements that matter most for the robot to succeed across varied conditions.

What would settle it

Apply the regularization to a manipulation task where human gaze consistently fixates on irrelevant scene elements and check whether success rates fall below the unregularized baseline.

Figures

Figures reproduced from arXiv: 2603.23202 by Anupam Pani, Yanchao Yang.

**Figure 1.** Figure 1: Effect of Gaze Regularization. The baseline (middle) exhibits scattered attention across the scene, while the gazeregularized model (right) concentrates on task-relevant regions (the plate and its immediate surroundings). This focused attention pattern not only improves task performance but also provides interpretable visual grounding that enhances trust in the model. sophistication and vast pretraining… view at source ↗

**Figure 2.** Figure 2: Overview of the Proposed Gaze-Regularized VLA Framework. Left: During training, gaze priors are converted into patchlevel gaze distributions that match the transformer’s attention resolution. The KL divergence between gaze and model attention is minimized, guiding the model to align its visual focus with human fixation patterns over time. Right: During inference, the policy operates without any gaze inpu… view at source ↗

**Figure 3.** Figure 3: Temporally Aggregated Gaze Prior Generation. A sequence of k video frames is tokenized and processed by the GLC [25] module, which predicts per-frame gaze heatmaps using both past and future context. These heatmaps are temporally aggregated to yield a gaze distribution that captures attention over time and serves as the supervision signal for training- time regularization. 3.3. Attention Modulation with Ga… view at source ↗

**Figure 4.** Figure 4: Closer look at Gaze Prior Generation A sequence of k video frames is tokenized and processed by the GLC [25] module, where it utilizes global tokens (derived from the sequence) and local tokens, and undergoes self attention as well as GlobalLocal Correlation to then predict per-frame gaze heatmaps. These heatmaps are temporally aggregated to yield a gaze distribution that captures attention over time and… view at source ↗

**Figure 5.** Figure 5: Additional Visualisations of Attention. Given the input observation, we show the spatial attention from the baseline model (second), the attention obtained when a perturbed gaze variant is used (third, corresponding to [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Reliability of Synthetic Gaze on Simulation Videos Given the input task, we show the the predicted gaze is accurate and even on similar visual settings, produces different gaze results depending on the language instruction. The model utilizes a temporal sequence of frames, rather than a single frame, and then computes the gaze prediction thus the prediction occurs due to the conditioning through the global… view at source ↗

**Figure 7.** Figure 7: Additional Visualisations of Attention. Given the input observation (first), we show the spatial attention from the baseline model (second) and task-relevant attention produced by our gaze-regularized model (third). D.5. Using Real Human Gaze for Fine-tuning GLC for Gaze Prediction To enable human-guided gaze prediction for simulation videos, we conducted a data collection study using a screenbased eye tr… view at source ↗

**Figure 8.** Figure 8: Visualization of Real-world Task on Aloha Robot In the figure, we provide some frames from a real world task performed using our gaze-regularized policy to show that our method works outside of simulation as well. Here, the task is to pick up the cube and place it on the correct plate. regularization into standard VLA optimization. E.1. Heatmap-to-Token Projection Pseudocode In this section, we provide pse… view at source ↗

**Figure 9.** Figure 9: Visualization of Real-world Task on Aloha Robot In this figure, we present a short horizon task of picking up a cup and placing it in a container(top) and also another longer horizon task to pick up multiple cups one-by-one, and place them in the container. Both visualisations are obtained using our gaze-regularized policy, highlighting its working functionality even in real-world scenarios [PITH_FULL_IMA… view at source ↗

**Figure 10.** Figure 10: Visualization Results In the figure, we provide some visualization results to show how the policy performs on the LiberoSpatial [29] task suites. We provide the task instructions, and some important frames to show the task success. The baseline model performs admirably, but our method enhances the results by using gaze-regularization [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗

**Figure 11.** Figure 11: Failure Case. We show a failure example from the Libero-Spatial [29] task suite. In this task, the baseline model outperforms the gaze-regularized model, suggesting that stronger or more accurate gaze priors could further improve reliability. The bottom sequence illustrates the failure case where the robot hand fails to grab the bowl in the top drawer and proceeds to carry out the intended action. 10 [PI… view at source ↗

**Figure 12.** Figure 12: Attention Comparison. The baseline model displays diffuse attention spread across the scene, with a single sharp point that is largely task-irrelevant. In contrast, the gaze-regularized model produces noticeably sharper, more concentrated, and consistently task-relevant attention, leading to clearer visual grounding for the instructed action. While promising, our method also opens several avenues for f… view at source ↗

**Figure 13.** Figure 13: Visualisation during a failure case. In this figure, we provide a visualisation of attention during a specific case of failure, where it can be seen that even though the task is to pick up the bowl, attention is not properly distributed on the bowl but rather than on the cabinet handle. Such cases can be mitigated using a better predictor or using a model trained with human supervision on simulated videos… view at source ↗

read the original abstract

Despite advances in Vision-Language-Action (VLA) models, robotic manipulation struggles with fine-grained tasks because current models lack mechanisms for active visual attention allocation. Human gaze naturally encodes intent, planning, and execution patterns -- offering a powerful supervisory signal for guiding robot perception. We introduce a gaze-regularized training framework that aligns VLA models' internal attention with human visual patterns without architectural modifications or inference-time overhead. Our method transforms temporally aggregated gaze heatmaps into patch-level distributions and regularizes the transformer's attention through KL divergence, creating an inductive bias toward task-relevant features while preserving deployment efficiency. When integrated into existing VLA architectures, our approach yields 4-12% improvements across manipulation benchmarks. The gaze-regularized models reach equivalent performance with fewer training steps and maintain robustness under lighting variations and sensor noise. Beyond performance metrics, the learned attention patterns produce interpretable visualizations that mirror human strategies, enhancing trust in robotic systems. Moreover, our framework requires no eye-tracking equipment and applies directly to existing datasets. These results demonstrate that human perceptual priors can significantly accelerate robot learning while improving both task performance and system interpretability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Gaze regularization via KL on patch distributions is a straightforward training addition to VLA models, but the 4-12% gains and robustness claims lack any experimental backing in the writeup.

read the letter

The main point is that the paper turns temporally aggregated human gaze heatmaps into patch-level distributions and uses KL divergence to pull the transformer's attention maps toward them during training. This adds no inference cost and works on existing datasets without needing eye tracking hardware at deployment time. The core idea is to inject a human perceptual prior as an inductive bias for fine-grained manipulation tasks in VLA architectures. What stands out is the clean implementation: no architecture changes, just an extra loss term that can be dropped in. If the gaze data really highlights task-relevant regions better than the model's default attention, this could explain faster convergence and the claimed performance lift. The interpretability side, where attention visualizations start resembling human strategies, follows naturally from the regularization. The soft spots are in the evidence. The abstract states 4-12% improvements across benchmarks plus robustness to lighting and sensor noise, yet supplies no baselines, no dataset descriptions, no ablation on the regularization strength, and no error analysis. Without those, the numbers cannot be assessed. The bigger assumption is that human gaze distributions are consistently closer to optimal robot attention than the unregularized model. In manipulation, humans often fixate on object affordances while the robot may need to track gripper kinematics, contact points, or occluded areas. If the benchmarks only cover cases where these align, the gains and robustness could be artifacts rather than a general property. The paper shows clear thinking on how to integrate the signal without circularity or extra parameters. It is aimed at VLA researchers who have or can obtain gaze-augmented demonstration data. A reader who wants a simple regularizer to test on standard manipulation suites would find the method description useful. It deserves a serious referee because the proposal is concrete and the claims are falsifiable with existing benchmarks, even if the current version needs expanded experiments.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces a gaze-regularized training framework for Vision-Language-Action (VLA) models in robotic manipulation. Aggregated human gaze heatmaps are converted to patch-level target distributions and used to regularize the transformer's attention maps via KL divergence, providing an inductive bias toward human-like visual patterns. The approach requires no architectural changes or inference overhead and is claimed to yield 4-12% performance gains on manipulation benchmarks, faster convergence to equivalent performance, robustness under lighting variations and sensor noise, and more interpretable attention maps, all while applying to existing datasets without new eye-tracking hardware.

Significance. If the results hold under rigorous evaluation, the work is significant for offering a lightweight, deployment-efficient mechanism to inject human perceptual priors into existing VLA architectures. This could accelerate learning in fine-grained manipulation tasks and improve system interpretability without hardware or runtime costs, representing a practical advance in leveraging human data for robot vision-language-action models.

major comments (2)

[Experiments] Experiments section: the central claim of 4-12% improvements and faster convergence is presented without baselines, number of runs, statistical tests, or error bars, rendering the performance gains unverifiable and load-bearing for the paper's contribution.
[Method and Experiments] Method and Experiments sections: the assumption that human gaze distributions are reliably closer to task-optimal attention than unregularized model attention is not tested on tasks where they diverge (e.g., gripper kinematics, force feedback regions, or occluded contacts); this distributional mismatch risk directly undermines the claimed generality of the inductive bias and robustness results.

minor comments (2)

[Abstract and Method] The abstract and method description introduce 'patch-level distributions' and KL regularization without an early equation or notation table, which would improve clarity for readers.
[Figures] Figure captions for attention visualizations should explicitly state the dataset, task, and comparison conditions to allow direct interpretation of the 'mirroring human strategies' claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below, indicating planned revisions where appropriate.

read point-by-point responses

Referee: [Experiments] Experiments section: the central claim of 4-12% improvements and faster convergence is presented without baselines, number of runs, statistical tests, or error bars, rendering the performance gains unverifiable and load-bearing for the paper's contribution.

Authors: We agree that the current presentation of results lacks sufficient statistical detail to fully verify the claims. In the revised manuscript, we will expand the Experiments section to report performance averaged over at least five independent training runs with different random seeds, include error bars showing standard deviation, explicitly list all baselines (including the unregularized VLA model), and apply statistical significance tests (e.g., paired t-tests) to support the reported 4-12% gains and faster convergence. These additions will make the empirical claims verifiable and robust. revision: yes
Referee: [Method and Experiments] Method and Experiments sections: the assumption that human gaze distributions are reliably closer to task-optimal attention than unregularized model attention is not tested on tasks where they diverge (e.g., gripper kinematics, force feedback regions, or occluded contacts); this distributional mismatch risk directly undermines the claimed generality of the inductive bias and robustness results.

Authors: This point highlights a valid limitation in the scope of our current evaluation. Our experiments are conducted on standard manipulation benchmarks from existing gaze-annotated datasets, where human gaze provides a demonstrably useful prior. We do not claim gaze is universally optimal across all possible robotic scenarios. In revision, we will add an explicit discussion of this assumption, acknowledge potential mismatches in tasks dominated by non-visual cues (such as force feedback or occluded contacts), and outline future work to validate on such cases. If feasible with available data, we will include supplementary analysis or proxy experiments to probe these edge cases. revision: partial

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper introduces an additive regularization framework that converts external human gaze heatmaps into patch-level targets and applies KL divergence to transformer attention maps within existing VLA architectures. No equations, parameters, or central claims reduce by construction to fitted inputs from the same paper or self-citations. Performance gains (4-12%) and robustness claims are presented as empirical outcomes of this independent inductive bias rather than tautological redefinitions or renamings of prior results. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that human gaze encodes useful task-relevant patterns for robotic perception; no free parameters or invented entities are introduced in the abstract.

axioms (1)

domain assumption Human gaze naturally encodes intent, planning, and execution patterns as a supervisory signal for robot perception.
Directly stated in the abstract as the foundation for the regularization approach.

pith-pipeline@v0.9.0 · 5491 in / 1160 out tokens · 33439 ms · 2026-05-15T00:24:07.509159+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

61 extracted references · 61 canonical work pages · 1 internal anchor

[1]

Admoni and B

H. Admoni and B. Scassellati. Social eye gaze in human- robot interaction: a review.Journal of Human-Robot Inter- action, 6:25, 2017. 2

work page 2017
[2]

Do as i can, not as i say: Grounding language in robotic affordances, 2022

Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Cheb- otar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Ir- pan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuan...

work page 2022
[3]

Eyes don’t lie: Subjective hate annotation and detection with gaze

¨Ozge Alacam, Sanne Hoeken, and Sina Zarrieß. Eyes don’t lie: Subjective hate annotation and detection with gaze. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 187–205, Miami, Florida, USA, 2024. Association for Computational Linguis- tics. 2

work page 2024
[4]

Anticipatory eye fixations reveal tool knowl- edge for tool interaction.Exp

Anna Belardinelli, Marissa Barabas, Marc Himmelbach, and Martin V Butz. Anticipatory eye fixations reveal tool knowl- edge for tool interaction.Exp. Brain Res., 234(8):2415– 2431, 2016. 2

work page 2016
[5]

It’s in the eyes: Planning precise manual actions before exe- cution.J

Anna Belardinelli, Madeleine Y Stepper, and Martin V Butz. It’s in the eyes: Planning precise manual actions before exe- cution.J. Vis., 16(1):18, 2016. 2

work page 2016
[6]

Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adrian Li-Bell, Mohith Mothukuri, Suraj Nair, Karl Pertsch, Lucy Xiaoyang Shi, James Tanner, Quan Vuong, Anna Walling, Haohuan Wang, and Ury Zhilinsky.π 0: A visi...

work page 2024
[7]

Rt-2: Vision-language-action mod- els transfer web knowledge to robotic control, 2023

Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakr- ishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil Joshi, Ryan Julian, Dmitry Kalashnik...

work page 2023
[8]

Rt-1: Robotics transformer for real- world control at scale, 2023

Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakr- ishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Ju- lian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalash- nikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav M...

work page 2023
[9]

From gaze to insight: Bridg- ing human visual attention and vision language model expla- nation for weakly-supervised medical image segmentation,

Jingkun Chen, Haoran Duan, Xiao Zhang, Boyan Gao, Vi- cente Grau, and Jungong Han. From gaze to insight: Bridg- ing human visual attention and vision language model expla- nation for weakly-supervised medical image segmentation,

work page
[10]

Look, focus, act: Efficient and robust robot learning via human gaze and foveated vision transformers, 2025

Ian Chuang, Jinyu Zou, Andrew Lee, Dechen Gao, and Iman Soltani. Look, focus, act: Efficient and robust robot learning via human gaze and foveated vision transformers, 2025. 2, 6

work page 2025
[11]

Embodiment Collaboration, Abby O’Neill, Abdul Rehman, Abhinav Gupta, Abhiram Maddukuri, Abhishek Gupta, Abhishek Padalkar, Abraham Lee, Acorn Pooley, Agrim Gupta, Ajay Mandlekar, Ajinkya Jain, Albert Tung, Alex Bewley, Alex Herzog, Alex Irpan, Alexander Khazatsky, Anant Rai, Anchit Gupta, Andrew Wang, Andrey Kolobov, Anikait Singh, Animesh Garg, Aniruddha...

work page 2025
[12]

Sam2act: Integrating visual foundation model with a mem- ory architecture for robotic manipulation, 2025

Haoquan Fang, Markus Grotz, Wilbert Pumacay, Yi Ru Wang, Dieter Fox, Ranjay Krishna, and Jiafei Duan. Sam2act: Integrating visual foundation model with a mem- ory architecture for robotic manipulation, 2025. 3

work page 2025
[13]

Bayliss, and Steven P

Alexandra Frischen, Andrew P. Bayliss, and Steven P. Tipper. Gaze cueing of attention: Visual attention, social cognition, and individual differences.Psychological Bulletin, 133(4): 694–724, 2007. 2

work page 2007
[14]

Haefflinger, F

L. Haefflinger, F. Elisei, S. Gerber, B. Bouchot, J. Vigne, and G. Bailly. On the benefit of independent control of head and eye movements of a social robot for multiparty human-robot interaction. pages 450–466, 2023. 2

work page 2023
[15]

Visual memory and motor planning in a natural task.J

Mary M Hayhoe, Anurag Shrivastava, Ryan Mruczek, and Jeff B Pelz. Visual memory and motor planning in a natural task.J. Vis., 3(1):49–63, 2003. 2

work page 2003
[16]

Huang, S

C. Huang, S. Andrist, A. Saupp ´e, and B. Mutlu. Using gaze patterns to predict task intent in collaboration.Frontiers in Psychology, 6, 2015. 2

work page 2015
[17]

Mutual context network for jointly estimating egocen- tric gaze and action.IEEE Transactions on Image Process- ing, 29:7795–7806, 2020

Yifei Huang, Minjie Cai, Zhenqiang Li, Feng Lu, and Yoichi Sato. Mutual context network for jointly estimating egocen- tric gaze and action.IEEE Transactions on Image Process- ing, 29:7795–7806, 2020. 2

work page 2020
[18]

Gaze-assisted medical image segmenta- tion, 2024

Leila Khaertdinova, Ilya Pershin, Tatiana Shmykova, and Bulat Ibragimov. Gaze-assisted medical image segmenta- tion, 2024. 2

work page 2024
[19]

Using human gaze to improve robustness against irrelevant objects in robot manipulation tasks.IEEE Robotics and Au- tomation Letters, 5(3):4415–4422, 2020

Heecheol Kim, Yoshiyuki Ohmura, and Yasuo Kuniyoshi. Using human gaze to improve robustness against irrelevant objects in robot manipulation tasks.IEEE Robotics and Au- tomation Letters, 5(3):4415–4422, 2020. 2

work page 2020
[20]

H. Kim, Y . Ohmura, and Y . Kuniyoshi. Gaze-based dual res- olution deep imitation learning for high-precision dexterous robot manipulation.Ieee Robotics and Automation Letters, 6:1630–1637, 2021. 2

work page 2021
[21]

Gaze-based dual resolution deep imitation learning for high- precision dexterous robot manipulation.IEEE Robotics and Automation Letters, 6(2):1630–1637, 2021

Heecheol Kim, Yoshiyuki Ohmura, and Yasuo Kuniyoshi. Gaze-based dual resolution deep imitation learning for high- precision dexterous robot manipulation.IEEE Robotics and Automation Letters, 6(2):1630–1637, 2021. 2

work page 2021
[22]

Multi-task real-robot data with gaze attention for dual-arm fine manipulation, 2024

Heecheol Kim, Yoshiyuki Ohmura, and Yasuo Kuniyoshi. Multi-task real-robot data with gaze attention for dual-arm fine manipulation, 2024. 6

work page 2024
[23]

Openvla: An open- source vision-language-action model, 2024

Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Fos- ter, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kol- lar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, and Chelsea Finn. Openvla: An open- source vision-language-action model, 2024. 1, 3, 6, 7

work page 2024
[24]

Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet, 2015

Matthias K ¨ummerer, Lucas Theis, and Matthias Bethge. Deep gaze i: Boosting saliency prediction with feature maps trained on imagenet, 2015. 2, 6

work page 2015
[25]

In the eye of transformer: Global-local correlation for egocentric gaze estimation.British Machine Vision Conference, 2022

Bolin Lai, Miao Liu, Fiona Ryan, and James Rehg. In the eye of transformer: Global-local correlation for egocentric gaze estimation.British Machine Vision Conference, 2022. 3, 5, 2, 7

work page 2022
[26]

Gaze-guided learning: Avoiding shortcut bias in visual classification, 2025

Jiahang Li, Shibo Xue, and Yong Su. Gaze-guided learning: Avoiding shortcut bias in visual classification, 2025. 2

work page 2025
[27]

Vision-language foun- dation models as effective robot imitators, 2024

Xinghang Li, Minghuan Liu, Hanbo Zhang, Cunjun Yu, Jie Xu, Hongtao Wu, Chilam Cheang, Ya Jing, Weinan Zhang, Huaping Liu, Hang Li, and Tao Kong. Vision-language foun- dation models as effective robot imitators, 2024. 1

work page 2024
[28]

Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In Proceedings of the European Conference on Computer Vi- sion (ECCV), 2018. 2

work page 2018
[29]

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowl- edge transfer for lifelong robot learning.arXiv preprint arXiv:2306.03310, 2023. 6, 7, 8, 3, 9, 10

work page internal anchor Pith review Pith/arXiv arXiv 2023
[30]

Robomamba: Efficient vision-language-action model for robotic reasoning and ma- nipulation, 2024

Jiaming Liu, Mengzhen Liu, Zhenyu Wang, Pengju An, Xi- aoqi Li, Kaichen Zhou, Senqiao Yang, Renrui Zhang, Yan- dong Guo, and Shanghang Zhang. Robomamba: Efficient vision-language-action model for robotic reasoning and ma- nipulation, 2024. 1, 3 10

work page 2024
[31]

Mindeye- omniassist: A gaze-driven llm-enhanced assistive robot sys- tem for implicit intention recognition and task execution,

Wei Luo, Bo Yang, Jian Huang, Haoyuan Wang, Zejia Zhang, Xinxing Chen, and Weizhuang Shi. Mindeye- omniassist: A gaze-driven llm-enhanced assistive robot sys- tem for implicit intention recognition and task execution,

work page
[32]

Mathew, Haithem Hermassi, Thariq Khalid, Ar- shad Ali Khan, and Riad Souissi

Athul M. Mathew, Haithem Hermassi, Thariq Khalid, Ar- shad Ali Khan, and Riad Souissi. Gazevlm: A vision- language model for multi-task gaze understanding, 2025. 2

work page 2025
[33]

Kyle Min and Jason J. Corso. Integrating human gaze into attention for egocentric activity recognition. In2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1068–1077, 2021. 2

work page 2021
[34]

Ovchinnikova, M

E. Ovchinnikova, M. W ¨achter, V . Wittenbeck, and T. Asfour. Multi-purpose natural language understanding linked to sen- sorimotor experience in humanoid robots. 2015. 2

work page 2015
[35]

Gaze-vlm: Bridging gaze and vlms through attention regularization for egocentric un- derstanding

Anupam Pani and Yanchao Yang. Gaze-vlm: Bridging gaze and vlms through attention regularization for egocentric un- derstanding. InThe Thirty-ninth Annual Conference on Neu- ral Information Processing Systems, 2025. 2, 1

work page 2025
[36]

10 open challenges steering the future of vision-language-action models, 2025

Soujanya Poria, Navonil Majumder, Chia-Yu Hung, Amir Ali Bagherzadeh, Chuan Li, Kenneth Kwok, Zi- wei Wang, Cheston Tan, Jiajun Wu, and David Hsu. 10 open challenges steering the future of vision-language-action models, 2025. 1

work page 2025
[37]

Human attention in fine-grained classification, 2021

Yao Rong, Wenjia Xu, Zeynep Akata, and Enkelejda Kas- neci. Human attention in fine-grained classification, 2021. 2

work page 2021
[38]

Human gaze follow- ing for human-robot interaction

Akanksha Saran, Srinjoy Majumdar, Elaine Schaertl Short, Andrea Thomaz, and Scott Niekum. Human gaze follow- ing for human-robot interaction. In2018 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), pages 8615–8621, 2018. 2

work page 2018
[39]

Efficiently guiding imitation learning agents with human gaze, 2021

Akanksha Saran, Ruohan Zhang, Elaine Schaertl Short, and Scott Niekum. Efficiently guiding imitation learning agents with human gaze, 2021. 2

work page 2021
[40]

VQA-MHUG: A gaze dataset to study multimodal neural attention in visual question answering

Ekta Sood, Fabian K ¨ogel, Florian Strohm, Prajit Dhar, and Andreas Bulling. VQA-MHUG: A gaze dataset to study multimodal neural attention in visual question answering. In Proceedings of the 25th Conference on Computational Nat- ural Language Learning, pages 27–43, Online, 2021. Asso- ciation for Computational Linguistics. 2

work page 2021
[41]

Enhancing reusability of learned skills for robot manipulation via gaze information and motion bottlenecks.IEEE Robotics and Automation Let- ters, 10(10):10737–10744, 2025

Ryo Takizawa, Izumi Karino, Koki Nakagawa, Yoshiyuki Ohmura, and Yasuo Kuniyoshi. Enhancing reusability of learned skills for robot manipulation via gaze information and motion bottlenecks.IEEE Robotics and Automation Let- ters, 10(10):10737–10744, 2025. 2

work page 2025
[42]

Gaze-guided task decomposition for imitation learning in robotic manipulation, 2025

Ryo Takizawa, Yoshiyuki Ohmura, and Yasuo Kuniyoshi. Gaze-guided task decomposition for imitation learning in robotic manipulation, 2025. 2

work page 2025
[43]

Octo: An open-source generalist robot policy, 2024

Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, and Sergey Levine. Octo: An open-source generalist robot policy, 2024. 3

work page 2024
[44]

Esfahani

Chaitanya Thammineni, Hemanth Manjunatha, and Ehsan T. Esfahani. Selective eye-gaze augmentation to enhance imi- tation learning in atari games, 2020. 2

work page 2020
[45]

Steven P. Tipper. Eps mid-career award 2009: From obser- vation to action simulation: The role of attention, eye-gaze, emotion, and body state.Quarterly Journal of Experimental Psychology, 63(11):2081–2105, 2010. 2

work page 2009
[46]

Gomez, Lukasz Kaiser, and Illia Polosukhin

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszko- reit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need, 2023. 1

work page 2023
[47]

Vlatest: Testing and evaluat- ing vision-language-action models for robotic manipulation

Zhijie Wang, Zhehua Zhou, Jiayang Song, Yuheng Huang, Zhan Shu, and Lei Ma. Vlatest: Testing and evaluat- ing vision-language-action models for robotic manipulation. Proceedings of the ACM on Software Engineering, 2(FSE): 1615–1638, 2025. 1

work page 2025
[48]

V oila-a: Aligning vision-language models with user’s gaze attention, 2023

Kun Yan, Lei Ji, Zeyu Wang, Yuntao Wang, Nan Duan, and Shuai Ma. V oila-a: Aligning vision-language models with user’s gaze attention, 2023. 2

work page 2023
[49]

Shen Yifan, Xiaoyu Mo, Vytas Krisciunas, David Hanson, and Bertram E. Shi. Intention estimation via gaze for robot guidance in hierarchical tasks. InProceedings of The 1st Gaze Meets ML workshop, pages 140–164. PMLR, 2023. 2

work page 2023
[50]

Whritner, Karl S

Ruohan Zhang, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. Agil: Learning attention from human for visuomotor tasks, 2018. 2

work page 2018
[51]

Muller, Jake A

Ruohan Zhang, Calen Walshe, Zhuode Liu, Lin Guan, Karl S. Muller, Jake A. Whritner, Luxin Zhang, Mary M. Hayhoe, and Dana H. Ballard. Atari-head: Atari human eye- tracking and demonstration dataset, 2019. 2

work page 2019
[52]

Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn

Tony Z. Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware, 2023. 6, 7

work page 2023
[53]

Vialm: A survey and benchmark of visually impaired assistance with large models, 2024

Yi Zhao, Yilin Zhang, Rong Xiang, Jing Li, and Hillming Li. Vialm: A survey and benchmark of visually impaired assistance with large models, 2024. 1

work page 2024
[54]

Learning from observer gaze:zero-shot attention prediction oriented by human-object interaction recognition, 2024

Yuchen Zhou, Linkai Liu, and Chao Gou. Learning from observer gaze:zero-shot attention prediction oriented by human-object interaction recognition, 2024. 2

work page 2024
[55]

Vip: Vision instructed pre-training for robotic manipu- lation.arXiv preprint arXiv:2410.07169, 2024

Li Zhuoling, Ren Liangliang, Yang Jinrong, Zhao Yong, et al. Vip: Vision instructed pre-training for robotic manipu- lation.arXiv preprint arXiv:2410.07169, 2024. 2, 6

work page arXiv 2024
[56]

Gaze-informed egocentric action recogni- tion for memory aid systems.IEEE Access, 6:12894–12904,

Zheming Zuo, Longzhi Yang, Yonghong Peng, Fei Chao, and Yanpeng Qu. Gaze-informed egocentric action recogni- tion for memory aid systems.IEEE Access, 6:12894–12904,

work page
[57]

pick” with “grab

2 11 Gaze-Regularized Vision-Language-Action Models for Robotic Manipulation Supplementary Material This supplementary document provides extended methodological details, additional ablations and implemen- tation clarifications to support the claims made in the main paper. The structure is as follows: • Appendix A: Notation Table • Appendix B: Expanded Met...

work page
[58]

2.Compute synthetic gaze heatmaps

Sample a batch of timesteps and episodes fromD: {(I1:n,t, ℓt, qt, A∗ t )}B b=1. 2.Compute synthetic gaze heatmaps. For each viewi∈ {1, . . . , n}and each example in the batch, construct a temporal window of frames: {Ii,t−T , . . . , Ii,t, . . . , Ii,t+T }. Pass this sequence through the GLC gaze model: [Hi,t−T , . . . , Hi,t]←ϕ gaze({Ii,t−T , . . . , Ii,t...

work page
[59]

Convert the aggregated heatmap ˜Hi,t into a patch-level distribution (Gi,t)

work page
[60]

St ={S i,t}n i=1, whereS i,t ∈R Nv is the spatial attention over visual tokens for viewi

Feed the multimodal observation into the VLA model: At =π θ(I1:n,t, ℓt, qt), obtaining predicted action sequencesA t. St ={S i,t}n i=1, whereS i,t ∈R Nv is the spatial attention over visual tokens for viewi

work page
[61]

Untilconvergence or maximum training steps

For each batch element and each view, compute the KL divergence between the gaze prior and the model attention. Untilconvergence or maximum training steps. Returnθ ∗. periments, motivated by the constraints of existing robotic 11 datasets. Comparisons against alternative gaze priors (e.g., uniform distributions or weaker gaze models) show that per- forman...

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