# Inside XRZero-G0, a new 2,000-hour open dataset for robotics research > Source: > Published: 2026-06-11 12:30:14+00:00 To break the data bottleneck slowing down embodied AI, X Square Robot said it has made XRZero-G0 open-source. The company said its new hardware-software framework reduces real-robot training data requirements by up to 20× under experimental conditions. Released alongside the G0-Dataset, a 2,000-hour multimodal repository, the system bridges the gap between human and [machine perception](https://www.therobotreport.com/category/design-development/ai-cognition/) by standardizing robot-free data collection, said X Square Robot. It said this allows human-demonstrated tasks to be reliably checked for quality and transferred to entirely unseen robotic platforms. The company described XRZero-G0 as a comprehensive hardware-software framework designed to enhance scalable, high-quality, robot-free data collection and cross-embodiment policy transfer for dexterous robotic manipulation. ## XRZero-G0 collects robot training data XRZero-G0 features an ergonomic, wearable virtual reality interface with multi-view [cameras](https://www.therobotreport.com/category/technologies/cameras-imaging-vision/) and specialized dual grippers to decouple human mobility from robot kinematics. X Square Robot said the system: - Uses a high-precision PICO 4 VR headset with inside-out spatial tracking - Equipped with dual physical [grippers](https://www.therobotreport.com/category/technologies/grippers-end-effectors/): an H-shaped press-actuated and a G-shaped finger-driven gripper - Supports millimeter-accurate 6-DoF pose estimation - Incorporates edge-side spatiotemporal parsing for synchronization of visual, language, and trajectory data - Ensures high collection throughput and stability, enabling sustained data capture without structural constraints Data quality has been a critical barrier in robot-free learning, noted X Square Robot. It said XRZero-G0 formalizes trainability governance via a closed-loop “collection–inspection–training–evaluation” pipeline: **Observation level:** Multi-view geometric consistency suppresses visual-kinematic misalignment.**Kinematic level:** Full-body inverse kinematics with collision and joint-limit constraints filter invalid trajectories.**Policy level:** Real-robot playback serves as the final validation criterion. ## X Square Robot validates X Square Robot said it has completed controlled experiments to prove that combining approximately 10 robot-free episodes with one real-robot episode can achieve performance comparable with purely real-robot datasets in evaluated tasks. The company has also scaled the G0-Dataset XRZero-G0 into a 2,000-hour dataset and open-sourced the result. The dataset integrates robot-free collection, automated quality inspection, mixed-data training, and real-robot evaluation for [research purposes](https://www.therobotreport.com/category/research-development/). G0-Dataset supports large-scale pretraining and cross-embodiment transfer experiments, providing a reproducible open resource for robotics research, explained X Square Robot. By open-sourcing XRZero-G0 and releasing G0-Dataset, the company said it provides hardware designs, automated inspection pipelines, training methodologies, and high-quality datasets to the research community. These resources are intended to accelerate the development of general-purpose robots and scalable embodied [AI](https://www.therobotreport.com/category/design-development/ai-cognition/), supporting a transition toward more systematic and large-scale data generation approaches. The full [research paper is available for download](https://arxiv.org/abs/2604.13001). The [code](https://github.com/X-Square-Robot/XRZero-G0) is available on GitHub, and the [Open Dataset](https://huggingface.co/datasets/x-square-robot/XRZero-G0-3K) is available on HuggingFace.