RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work
Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to understand what the worker is doing, seeing through the human's camera or smart glasses, and assist them in documentation.
This article introduces RealityBench, a simulated environment designed to evaluate the ability of vision language models to document human physical work. The study focuses on assessing how well these models can understand and document tasks in real-world settings, such as rooftop solar installation and vehicle repair.
What happened
The article presents the development of RealityBench, a simulated environment using Blender to evaluate vision language models' ability to document physical work tasks. The study tests these models across 26 vision-language models in various environments, focusing on documentation and acceptability tasks. The findings indicate that while top models perform well in certain aspects, they face challenges in others, such as reading measurements and spatial relations.
Why it matters
This research is important for AI developers and technology professionals as it addresses the challenges of evaluating vision language models in real-world, high-variance environments. Understanding these limitations can help improve the accuracy and reliability of AI systems used in physical work documentation.
- RealityBench offers a new method for evaluating vision language models in real-world tasks.
- Top models excel in text-based documentation but struggle with measurements and spatial relations.
- Human rater disagreement highlights the subjectivity of acceptability assessments.
- Model performance on simulated data can translate to real-world scenarios in some cases.
- The research underscores the need for better benchmarks in high-variance environments.
The bigger picture
The study provides insights into the current capabilities and limitations of vision language models in real-world applications. This can guide future research and development in improving AI systems for physical work documentation, particularly in high-variance environments.
RealityBench is a significant contribution to evaluating vision language models in real-world physical work tasks. The study reveals both the strengths and limitations of current models, emphasizing the need for continued research in this area.
Technical details
- Blender simulations for creating realistic environments
- Vision language models (VLMs) tested across 26 models
- Documentation tasks involving text in frame, measurements, and spatial relations
- Acceptability tasks assessing view suitability for documentation
- Model rankings on rendered frames transferring to real images
FAQ
What is RealityBench?
RealityBench is a simulated environment created in Blender to evaluate the ability of vision language models to document human physical work tasks.
What are the main challenges in benchmarking vision language models for physical work tasks?
The main challenges include the high variance and unconstrained nature of real-world environments, the subjectivity of acceptability assessments, and the difficulty of obtaining proprietary data for testing.
How do vision language models perform in documentation tasks according to the study?
Top models perform well in documentation tasks when the answer is text in frame but struggle with measurements, small parts, and spatial relations.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.CV (CV) on Oct 14, 2026.
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