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All Robots in One: A New Standard and Unified Dataset for Versatile, General-Purpose Embodied Agents
Zhiqiang Wang, Hao Zheng, Yunshuang Nie, Wenjun Xu, Qingwei Wang, Hua Ye, Zhe Li, Kaidong Zhang, Xuewen Cheng, Wanxi Dong, Chang Cai, Liang Lin, Feng Zheng, Xiaodan Liang
TL;DR
Existing embodied-AI datasets lack standardized formats, comprehensive modalities, sufficient scale, and integrated simulated and real-world data. ARIO introduces a unified data standard and dataset spanning diverse robots, tasks, modalities, and environments. The resulting resource contains 3,033,188 episodes from 258 series and 321,064 tasks, while evaluation of its effectiveness for large-scale model training remains future work.
Problem
Existing embodied-AI datasets lack standardized formats, comprehensive sensory modalities, sufficient data volume, and combined simulated and real-world data.
Source: §1 · p. 1, §3.2 · p. 4Method
ARIO provides a unified standard and dataset combining timestamped multimodal data from real-world collection, simulation platforms, and converted open-source datasets.
Source: §1 · p. 1, §3.3.2 · p. 6, §3.3.3 · p. 7Results
The ARIO dataset contains 3,033,188 episodes from 258 series and 321,064 tasks.
Source: §3.3.5 · p. 8Takeaways & Limitations
ARIO provides a cohesive framework for data collection and representation across diverse embodied-AI tasks, environments, and robot types.
Source: §1 · p. 1The effectiveness of ARIO for training large-scale embodied-AI models remains to be evaluated.
Source: §4 · p. 11
Abstract
from arXiv · showhide
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/
1. Introduction
ARIO addresses shortcomings in embodied-AI datasets through a unified standard and a large-scale dataset combining diverse modalities, robot types, and simulated and real-world data.
Motivation
- ARIO addresses missing sensory coverage, nonstandard formats, cross-platform incompatibility, limited data volume, and insufficiently integrated simulated and real data.These limitations hinder development of versatile, general-purpose embodied agents.Source: §1 · p. 1
ARIO standard
- ARIO standardizes control and motion data across robot morphologies with timestamp-based synchronization for differing action and sensor rates.Its unified format supports variable data from different robot types.Source: §1 · pp. 1–2
Dataset
- The ARIO dataset contains approximately 3 million episodes from 258 series and 321,064 tasks, assembled from real-world collection, simulation, and converted open-source datasets.The sources contribute 3,662 real-world episodes, 703,088 simulated episodes, and 2,326,438 converted episodes.Source: §1 · pp. 1–2
- ARIO incorporates images, 3D data, sound, text, and tactile information across multiple robot types, tasks, and scenarios.This combination is intended to support richer and more diverse embodied-AI data.Source: §1 · p. 2
ARIO standard
- The standard organizes data through a structured series-task-episode framework with detailed textual descriptions.This structure is complemented by cleaning and standardization of large open-source datasets.Source: §1 · p. 2
2. Related Work
Prior embodied-robotics datasets advance large-scale learning but remain limited in sensory richness, cross-platform standardization, multimodal integration, and scalable language-guided collection.
Large-Scale Robotic Learning Datasets
- Existing large-scale robotics datasets often emphasize visual manipulation data while offering limited task diversity and little tactile, auditory, or proprioceptive feedback.ARIO incorporates image, 3D vision, audio, text, and tactile modalities.Source: §2 · p. 2
Multi-Embodiment and Cross-Platform Learning
- Prior multi-embodiment work seeks transfer across robotic platforms, but datasets such as Open X-Embodiment often lack standardized formats.ARIO combines standardized formats with simulated and real-world data for examining sim-to-real transition.Source: §2 · pp. 2–3
Multi-Modal Learning and Sensory Integration
Language-Guided Robot Learning
- Language-guided robot learning uses language models and human annotations, but its data collection remains largely dependent on human input.ARIO provides a standardized multimodal dataset for diverse and complex embodied tasks.Source: §2 · p. 3
3.1. What is the ARIO Standard?
The ARIO standard structures embodied-AI data collection, storage, and analysis across environments and tasks through hierarchical organization, synchronized modalities, and comprehensive metadata.
Hierarchical Data Structure
- ARIO organizes data into collection, series, task, and episode layers, with each episode containing synchronized observation and control data.Series align scenes with robot types, while tasks include natural-language instructions.Source: §3.1 · p. 3
Data Collection Protocols
- The standard mandates broad environment and action coverage while recording text instructions, images, and task-specific robot-state modalities.Examples include end-effector states and navigational data.Source: §3.1 · p. 3
Metadata and Documentation
- ARIO supplies series-level and task-level YAML metadata describing scenes, robots, sensors, instructions, and required skills.These files align metadata with the corresponding episode data.Source: §3.1 · p. 3
Standardization and Integrity
- Standardized formats and collection protocols improve data integration and analysis for adaptable, scalable robotic agents.The paper links structured data handling with enhanced robotic learning and generalization across scenarios and tasks.Source: §3.1 · p. 3
3.2. Why do we need ARIO?
ARIO is needed because embodied-intelligence data must combine diverse robot forms, synchronized temporal information, rich sensory modalities, and interoperable formats across platforms. Existing datasets remain fragmented across these dimensions, including a persistent gap between simulated and real data.
- Diverse robot morphologies require different control and motion representations, preventing a universal format across single-arm, dual-arm, humanoid, and wheeled systems.Source: §3.2 · p. 4
- Variable sensor frame rates and robot action frequencies make timestamping essential for sequencing sensory inputs and control outputs.Source: §3.2 · p. 4
- Existing embodied-intelligence datasets lack simultaneous images, 3D structures, text, tactile, and auditory inputs.Source: §3.2 · p. 4
- Existing datasets lack unified formats for processing diverse robotic forms and control objects, from mobile navigation to arm positions and joint angles.Source: §3.2 · p. 4
- Available datasets generally do not provide both simulated and real data for the same robot or simulation data based on real-world scans.Source: §3.2 · p. 4
- ARIO therefore targets a voluminous, varied, timestamped standard that adapts across robot morphologies while integrating real and simulated data.Source: §3.2 · p. 4
3.3. How do we build ARIO dataset?
ARIO is built as a unified embodied-AI dataset through standardized multimodal design, real-world collection, simulation, and conversion of open-source data. Its resulting statistics show broad coverage across tasks, scenes, skills, data sources, and robot configurations.
3.3. How do we build ARIO dataset?
- ARIO organizes collection into real-world scenarios, simulation platforms, and transformations of open-source datasets.The pipeline is explicitly composed of three parallel components.Source: §3.3 · p. 4
3.3.1 Design requirements
- Five modalities, timestamp alignment, scene-task-episode organization, and uniform configuration files support diverse robots, actions, and data sources.The design includes 2D images, 3D vision, sound, text, and tactile data, with sensor-specific recording rates.Source: §3.3.1 · p. 4
3.3.2 Collection from real-world scenarios
- Cobot Magic designs over 60 tabletop manipulation tasks in household settings, spanning long-horizon, bimanual and fine manipulation, contact-rich, human-robot collaboration, and deformable-object manipulation.Source: §3.3.2 · p. 6
3.3.3 Generation from simulation platforms
- Simulation contributes Habitat navigation, MuJoCo manipulation, and SeaWave language-instruction tasks, with Habitat recording RGB-D observations and agent states.MuJoCo generates 1,700 trajectory episodes involving 21 interactive objects.Source: §3.3.3 · p. 7
3.3.4 Transformation from open source datasets
- Conversion tools standardize Open X-Embodiment, RH20T, and ManiWAV data while preserving usable records and adding textual descriptions where applicable.The process converted 62 Open X-Embodiment datasets, 12,719 RH20T trajectories, and 1,297 ManiWAV trajectories, while excluding records with missing essential information.Source: §3.3.4 · pp. 7–8
3.3.5 Data statistics
- 3,033,188 episodes across 258 series and 321,064 tasks cover diverse indoor scenes, manipulation and navigation skills, data sources, and robot parameters.The statistics include robot morphologies, motion objects, physical variables, sensors, viewpoints, control modes, operation modes, and arm-joint counts.Source: §3.3.5 · pp. 8–11
4. Future work
The paper identifies future work in evaluating ARIO, broadening data diversity, aligning real and simulated data, and scaling embodied-AI datasets. These directions target stronger generalization, transfer, and capability in physical-world interaction.
Evaluating ARIO for Large-Scale Model Training
- ARIO still requires evaluation for large-scale embodied-AI model training across varied tasks to assess its potential for improving generalization and robustness.The proposed evaluation may use existing open-source models or specialized architectures.Source: §4 · p. 11
Expanding Data Diversity
- Further data collection should add richer tactile, audio, and point-cloud modalities for more comprehensive physical-world perception and interaction.Source: §4 · p. 11
- Collecting data from the same robots in simulated and real-world settings would support methods that bridge the simulation-to-reality gap and improve transferability.Source: §4 · p. 11
Scaling Up Embodied AI Data
- Large-scale simulated environments and multi-robot interaction scenarios could generate massive datasets for collaborative and coordinated embodied-AI behavior.The proposed simulated environments may contain diverse robots performing different tasks and generate millions or billions of data points.Source: §4 · p. 11