Robo2vlm Reasoning

Robo2VLM Reasoning is a dataset designed to test the ability of vision-language models to perform reasoning based on natural robotic scenes. It offers reasoning traces generated automatically from visual contexts, to enrich the analysis and understanding of object manipulation scenes.

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Size

Several thousand multimodal examples with images, questions and textual reasoning generated

Licence

Apache 2.0

Description

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Robo2vlm Reasoning is an extension of the Robo2VLM dataset, focused on visual reasoning skills. It uses robotic in-the-wild scenes combined with visual questions and answers justified by generated reasoning. This benchmark makes it possible to assess the coherence and logic of the answers provided by multimodal models.

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What is this dataset for?

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Test the visual reasoning skills of VLMs (Vision-Language Models)
Improving the contextual understanding of robotic scenes by AIs
Evaluate the ability to justify answers through coherent lines of reasoning

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Can it be enriched or improved?

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Yes, this dataset can be enriched by additional human annotations, linguistic variants, or by adding error cases to train robustness. It can also be extended to other types of tasks such as planning or anticipating robotic actions.

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🔎 In summary

Criterion	Evaluation
🧩 Ease of use	⭐⭐⭐✩✩ (Readable format, but requires multimodal preprocessing)
🧼 Need for cleaning	⭐⭐⭐⭐⭐ (Low – data generated in a controlled way)
🏷️ Annotation richness	⭐⭐⭐⭐✩ (Good – each question accompanied by reasoning trace)
📜 Commercial license	✅ Yes (Apache 2.0)
👨‍💻 Beginner friendly	⚠️ Moderate – handling multimodal data requires solid foundations
🔁 Fine-tuning ready	🎯 Perfect for VQA or multimodal reasoning model training
🌍 Cultural diversity	⚠️ Limited – scenes centered on standard robotic environments