EgoSuite-Open100K - A Full-Modality Human Behavior Dataset Open-Sourced by Guanglun Intelligence

What is EgoSuite-Open100K

EgoSuite-Open100K is the world's first 100,000-hour-level full-modality human behavior open-source dataset launched by Guanglun Intelligence, aimed at physical AI and embodied intelligence. The dataset contains first-person videos from 15,000+ real-world scenarios, covering 7 major categories of environments including home and industrial settings. It uses dual-perspective collection from head and wrist, and is equipped with three layers of fine-grained annotations: 21-joint hand poses, body posture, and event semantics. The dataset is freely available on Hugging Face and AtomGit, supports academic and commercial use, and can solve the bottleneck of the scarcest high-quality human behavior data in robot training.

Main Features of EgoSuite-Open100K

• Large-Scale Human Behavior Collection: Provides 100,000 hours of first-person real human manipulation videos, offering sufficient behavior prior data for physical AI training.
• Dual-Perspective Synchronous Recording: Uses a combination of a head-mounted main perspective and a wrist close-up perspective, ensuring both global action continuity and capturing fine finger manipulation details.
• Three-Layer Full-Modality Annotation: Frame-by-frame annotation of 21-joint hand poses, full-body posture, and event-level semantic information, enabling the model to learn both "how to move" and "why to move."
• Cross-Scenario Generalization Coverage: Covers 7 major categories of environments including home, industrial, and medical, with 128 scenario types, breaking through the limitation of existing datasets that are limited to a single kitchen scenario.
• Open Source and Commercial-Friendly: Fully open on Hugging Face and AtomGit, free to use for both academic research and commercial training, breaking data monopolies.
• Unified Data Standards: Follows the EgoVerse International Data Committee specifications, solving the industry pain point of incompatible data formats across different devices and teams.
• Supports Cross-Embodiment Transfer: With data volume reaching the 100,000-hour level, it enables models to generalize human actions to robot bodies of different forms.
• Closed-Loop Ecosystem Foundation: Linked with RoboFinals simulation evaluation and RoboStack real-world deployment, forming a continuous learning infrastructure of "collection-training-evaluation-feedback."

Technical Principles of EgoSuite-Open100K

• Data Scale and Scaling Law: Based on the physical AI Scaling Law, the dataset scale reaches 100,000 hours of human first-person videos, at a critical inflection point for the emergence of cross-embodiment transfer capabilities.
• Dual-Perspective Collection Architecture: Uses synchronous dual-perspective collection with a head-mounted main perspective and a wrist close-up perspective, capturing global action context and fine finger manipulation details respectively.
• Full-Modality Aligned Annotation Performs full-modality aligned annotation on videos, including 21-joint hand poses, body posture, event-level semantics, and depth information, constructing multimodal training signals.
• Standardized Data Format: Follows the EgoVerse international data standard to unify collection formats and annotation specifications, enabling data from different sources to have cross-device and cross-model reusability.
• Continuous Learning Loop: Linked with the SimFoundry simulation platform and the RoboStack deployment system, forming a continuous learning loop of human data pre-training, simulation evaluation and validation, and real-scenario feedback iteration.

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How to Use EgoSuite-Open100K

• Get the Data: Access the first batch of open-source data via Hugging Face Hub or AtomGit, available for direct use in academic or commercial model training without application.
• Select Data Subset: Choose EgoStandard (head-mounted first-person view) or EgoPro (wrist close-up view) based on task requirements, suitable for full-body motion learning or fine manipulation grasping, respectively.
• Parse Multi-layer Annotations: Utilize the accompanying 21-joint hand pose, body posture, and event-level semantic annotations to construct multimodal input signals for policy model or VLA model training.
• Integrate into Training Pipeline: Input the dataset as pre-training corpus into the robot policy network, leveraging 100,000 hours of human behavior priors to enhance the model's generalization ability in real-world scenarios.
• Combine with Simulation Validation: Through the Guanglun Intelligent SimFoundry simulation platform or the RoboFinals evaluation system, conduct low-cost, reproducible, large-scale capability validation of models trained on this dataset.