From open benchmark datasets to custom collection pipelines, RealSource delivers standardized real-world robot data at scale.
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_east3 camera views · hardware-level sync · precise timestamps
Tightly aligned multi-view visual streams give models a consistent temporal foundation for perception, fusion, and action learning.
<0.5% loss rate across multi-channel collection
An optimized transmission, caching, and processing pipeline helps preserve complete and continuous demonstrations even under demanding recording conditions.
71-D state space · 14M+ frames · 30 FPS visual observations
RealSource pairs dense robot proprioception with large-scale visual demonstrations to support both learning and evaluation workflows.
Full intrinsics/extrinsics · ready for correction, hand-eye, and 3D workflows
With complete calibration metadata included, teams can move faster from raw data to usable perception and reconstruction pipelines.
35 tasks · kitchen / conference room / convenience store / household scenes
RealSource introduces controlled variation in objects, scenes, lighting, viewpoints, and execution styles so models learn robust behaviors instead of brittle demonstrations.
Exoskeleton teleop · 7 DoF · 0.088° · 100 Hz
Exoskeleton-based teleoperation preserves natural dual-arm coordination and expert motion intent, creating trajectories that are more transferable to real embodied tasks.
Standardized for Real-World AI Pipelines
From data collection to model training and deployment, RealSource is built for compatibility, consistency, and workflow integration.
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.
A standardized workflow for turning task requirements into training-ready, deployment-aligned datasets.
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.