Publicly available trajectories distributed across official repositories for open research and benchmarking.
Get Data north_eastStandardized task and scene datasets designed for faster model development and evaluation.
Request Spec north_eastCustom data collection pipelines tailored to your research objectives, robot setup, and target scenarios.
Talk to Sales north_eastTightly aligned multi-view visual streams give models a consistent temporal foundation for perception, fusion, and action learning.
RealSource pairs dense robot proprioception with large-scale visual demonstrations to support both learning and evaluation workflows.
Each robot and sensor undergoes rigorous factory calibration, with complete camera calibration parameters provided, enabling users to deploy the system directly without additional calibration.
For the same task, large-scale demonstrations are performed across diverse variables, including object attributes, environmental context, motion trajectories, and viewpoints, to support broader generalization.
An optimized transmission, caching, and processing pipeline helps preserve complete and continuous demonstrations even under demanding recording conditions.
Exoskeleton-based teleoperation preserves natural dual-arm coordination and expert motion intent, creating trajectories that are more transferable to real embodied tasks.
RealMan leads the formulation of 3 CR standards for humanoid robot data systems.
Supports MCAP, HDF5, and LeRobot for easier integration with mainstream research toolchains.
Designed for reinforcement learning validation, model development, and real-world deployment.
The Fuel of the Embodied Intelligence Era
Embodied Manipulation Systems
Embodied Data Collection Units
Multimodal Data Output
Industry Enablement
Set target tasks, scenarios, data scope, and success criteria.
Confirm robot configuration, sensors, environments, and collection strategy.
Run parallel teleoperation sessions for scalable real-world data collection.
Apply automated checks and expert review to ensure consistency and usability.
Prepare the dataset in target formats such as MCAP, HDF5, or LeRobot.
Add semantic labels, metadata, and task structure for downstream training use.
Deliver packaged datasets for easier use in research, training, and deployment workflows.
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