Galaxea Open-World Dataset and G0 Dual-System VLA Model

• URL: http://arxiv.org/abs/2509.00576v1
• Date: Sat, 30 Aug 2025 18:04:19 GMT
• Title: Galaxea Open-World Dataset and G0 Dual-System VLA Model
• Authors: Tao Jiang, Tianyuan Yuan, Yicheng Liu, Chenhao Lu, Jianning Cui, Xiao Liu, Shuiqi Cheng, Jiyang Gao, Huazhe Xu, Hang Zhao,
• Abstract summary: We present a large-scale, diverse collection of robot behaviors recorded in authentic human living and working environments.<n>All demonstrations are gathered using a consistent robotic embodiment, paired with precise subtask-level language annotations.<n>G0 is trained using a three-stage curriculum: cross-embodiment pre-training, single-embodiment pre-training, and task-specific post-training.
• Score: 55.756245350141675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present Galaxea Open-World Dataset, a large-scale, diverse collection of robot behaviors recorded in authentic human living and working environments. All demonstrations are gathered using a consistent robotic embodiment, paired with precise subtask-level language annotations to facilitate both training and evaluation. Building on this dataset, we introduce G0, a dual-system framework that couples a Vision-Language Model (VLM) for multimodal planning with a Vision-Language-Action (VLA) model for fine-grained execution. G0 is trained using a three-stage curriculum: cross-embodiment pre-training, single-embodiment pre-training, and task-specific post-training. A comprehensive benchmark spanning tabletop manipulation, few-shot learning, and long-horizon mobile manipulation, demonstrates the effectiveness of our approach. In particular, we find that the single-embodiment pre-training stage, together with the Galaxea Open-World Dataset, plays a critical role in achieving strong performance.

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