RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
RoboGaze is a training-free, multi-agent VLM framework that provides structured, interpretable evaluation for generated robot-manipulation videos. It uses a three-stage pipeline and outputs localized glitch reports under a novel taxonomy, outperforming zero-shot baselines by large margins.
[2606.28385] RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
[Submitted on 22 Jun 2026]
Title:RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
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Abstract:Recent advances in robot world models enable synthetic video generation for embodied prediction and planning. However, evaluating these videos is challenging: visually realistic outputs often violate physical laws, temporal consistency, or task logic, while conventional metrics and monolithic Vision-Language Model (VLM) judges fail to generalize or provide precise diagnostic value. We present RoboGaze, a training-free, multi-agent VLM framework that provides structured, interpretable evaluation for generated robot-manipulation videos. Given a task instruction and video, RoboGaze operates via a three-stage pipeline: task-scene grounding, dimension-specific specialist routing, and critic-based verification. It outputs temporally localized glitch reports categorized under a novel 6-dimension, 30-type robotics-specific taxonomy. To benchmark RoboGaze, we introduce a human-validated dataset of 382 clips spanning simulated and real-world multi-view manipulation. Evaluating eight open-source and proprietary VLM backbones, RoboGaze dramatically outperforms zero-shot baselines, improving description-F1 by up to +43 points and temporal alignment (F1 x IoU) by up to +37 points, closing approximately 85% of the gap to the human ceiling. Furthermore, its critic verifier mitigates the "cry-wolf" false-positive flaw of standard VLMs, lifting clean-clip accuracy from under 25% to over 80%. RoboGaze offers a scalable, highly interpretable diagnostic tool for the rigorous evaluation of robot world models.
Comments: First version 29 pages, 7 figures. Project webpage: this https URL
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.28385 [cs.RO]
(or arXiv:2606.28385v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28385
arXiv-issued DOI via DataCite
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From: Minh-Loi Nguyen [view email] [v1] Mon, 22 Jun 2026 06:45:09 UTC (37,091 KB)
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