# XRZero-G0: Pushing the Frontier of Dexterous Robotic Manipulation with Interfaces, Quality and Ratios

arXiv: https://arxiv.org/abs/2604.13001 · HTML: https://mlsanity.com/paper/2604.13001/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2026.

## Built on by (most cited first, 5 of 5)

- [WALL-WM: Carving World Action Modeling at the Event Joints](https://mlsanity.com/paper/2606.01955/) (2026)
- [Wall-OSS-0.5 Technical Report](https://mlsanity.com/paper/2605.30877/) (2026)
- [HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone](https://mlsanity.com/paper/2607.25895/) (2026)
- [VISTA: Vision-Grounded and Physics-Validated Adaptation of UMI data for VLA Training](https://mlsanity.com/paper/2606.04708/) (2026)
- [EgoRecovery: Acquiring Failure Recovery Ability Through Human Recovery Demonstration](https://mlsanity.com/paper/2607.19745/) (2026)

## Abstract (arXiv)

The acquisition of high-quality, action-aligned demonstration data remains a fundamental bottleneck in scaling foundation models for dexterous robot manipulation. Although robot-free human demonstrations (e.g., the UMI paradigm) offer a scalable alternative to traditional teleoperation, current systems are constrained by sub-optimal hardware ergonomics, open-loop workflows, and a lack of systematic data-mixing strategies. To address these limitations, we present XRZero-G0, a hardware-software co-designed system for embodied data collection and policy learning. The system features an ergonomic, virtual reality interface equipped with a top-view camera and dual specialized grippers to directly improve collection efficiency. To ensure dataset reliability, we propose a closed-loop collection, inspection, training, and evaluation pipeline for non-proprioceptive data. This workflow achieves an 85% data validity rate and establishes a transparent mechanism for quality control. Furthermore, we investigate the empirical scaling behaviors and optimal mixing ratios of robot-free data. Extensive experiments indicate that combining a minimal volume of real-robot data with large-scale robot-free data (e.g., a 10:1 ratio) achieves performance comparable to exclusively real-robot datasets, while reducing acquisition costs by a factor of twenty. Utilizing XRZero-G0, we construct a 2,000-hour robot-free dataset that enables zero-shot cross-embodiment transfer to a target physical robot, demonstrating a highly scalable methodology for generalized real-world manipulation.Our project repository: https://github.com/X-Square-Robot/XRZero-G0