# All Robots in One: A New Standard and Unified Dataset for Versatile, General-Purpose Embodied Agents

arXiv: https://arxiv.org/abs/2408.10899 · HTML: https://mlsanity.com/paper/2408.10899/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2024.

## Built on by (most cited first, 15 of 15)

- [RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation](https://mlsanity.com/paper/2410.07864/) (2024)
- [RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation](https://mlsanity.com/paper/2412.13877/) (2024)
- [World Action Models: The Next Frontier in Embodied AI](https://mlsanity.com/paper/2605.12090/) (2026)
- [Vision Language Action Models in Robotic Manipulation: A Systematic Review](https://mlsanity.com/paper/2507.10672/) (2025)
- [Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities](https://mlsanity.com/paper/2507.13019/) (2025)
- [Is Diversity All You Need for Scalable Robotic Manipulation?](https://mlsanity.com/paper/2507.06219/) (2025)
- [DeepThinkVLA: Enhancing Reasoning Capability of Vision-Language-Action Models](https://mlsanity.com/paper/2511.15669/) (2025)
- [Survey of Vision-Language-Action Models for Embodied Manipulation](https://mlsanity.com/paper/2508.15201/) (2025)
- [Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction](https://mlsanity.com/paper/2503.05231/) (2025)
- [RGC-VQA: An Exploration Database for Robotic-Generated Video Quality Assessment](https://mlsanity.com/paper/2506.23852/) (2025)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [Audio-Visual Intelligence in Large Foundation Models](https://mlsanity.com/paper/2605.04045/) (2026)
- [Embodied AI in Machine Learning -- is it Really Embodied?](https://mlsanity.com/paper/2505.10705/) (2025)
- [Embodied Operators and Benchmarking: Toward Reusable and Deployable Embodied Intelligence Systems](https://mlsanity.com/paper/2607.03283/) (2026)
- [NarrativeFlow: Flow-Based Vision-Language-Action Model Using Robot Velocity Fields](https://mlsanity.com/paper/2610.00981/) (2026)

## Abstract (arXiv)

Embodied AI is transforming how AI systems interact with the physical world, yet existing datasets are inadequate for developing versatile, general-purpose agents. These limitations include a lack of standardized formats, insufficient data diversity, and inadequate data volume. To address these issues, we introduce ARIO (All Robots In One), a new data standard that enhances existing datasets by offering a unified data format, comprehensive sensory modalities, and a combination of real-world and simulated data. ARIO aims to improve the training of embodied AI agents, increasing their robustness and adaptability across various tasks and environments. Building upon the proposed new standard, we present a large-scale unified ARIO dataset, comprising approximately 3 million episodes collected from 258 series and 321,064 tasks. The ARIO standard and dataset represent a significant step towards bridging the gaps of existing data resources. By providing a cohesive framework for data collection and representation, ARIO paves the way for the development of more powerful and versatile embodied AI agents, capable of navigating and interacting with the physical world in increasingly complex and diverse ways. The project is available on https://imaei.github.io/project_pages/ario/