RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis
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
Record details
Published: 22 June 2026
Source: arXiv
Category: Research
Topics: Healthcare · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (22 June 2026), “RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis,” evidence record 718, https://ethics.ai/record/718 (originally published by arXiv).
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