开源机器人平台+视觉语言动作模型，实现实体智能低成本落地
OpenEAI-Platform: An Open-source Embodied Artificial Intelligence Hardware-Software Unified Platform
- 自研6+1自由度机械臂+可复现的视觉语言动作模型
- 在4项真实任务中性能超越商用机械臂，成功率媲美大模型
- 适合机器人、具身智能研究者快速搭建实验平台
实体人工智能在真实世界应用需要精确硬件与鲁棒的视觉-语言-动作（VLA）策略。本文提出OpenEAI-Platform，一个全开源的软硬件一体化平台，包含低成本的6+1自由度机械臂（OpenEAI-Arm）和可复现的VLA模型（OpenEAI-VLA）。OpenEAI-Arm提供开源机械设计以降低制造成本，并采用顺应性控制方法提升精度；OpenEAI-VLA基于Qwen3-VL-4B，采用扩散变换器动作头，仅使用开源机器人与多模态数据集分两阶段训练。在四项真实世界操作任务中，OpenEAI-Arm在相同策略下表现优于两款商用6+1自由度机械臂，OpenEAI-VLA仅用有限预训练数据即达到与大规模预训练pi0基线相当的成功率。我们将公开完整硬件设计、驱动程序、模型及训练/数据流水线，支持可复现研究与规模化数据采集。代码、布局图与模型将在论文录用后发布。
原文摘要 · Abstract (English)
Embodied AI in the real world requires both accurate hardware and robust vision-language-action (VLA) policies. We present OpenEAI-Platform, a fully open-source platform that integrates a low-cost 6+1 degree-of-freedom (dof) robotic arm (OpenEAI-Arm) and a reproducible VLA model (OpenEAI-VLA). OpenEAI-Arm provides open-source mechanical designs for low manufacturing cost and compliant control methods for higher accuracy. OpenEAI-VLA builds on Qwen3-VL-4B and uses a Diffusion Transformer action head, and is trained in two stages with only open-source robot and multimodal datasets. Across four real-world manipulation tasks, OpenEAI-Arm outperforms two commercial 6+1-dof arms under the same policy, and OpenEAI-VLA achieves success rates comparable to the large-scale pretrained pi0 baseline with only limited pretraining data. We will release the full hardware designs, drivers, models, and training/data pipelines to support reproducible research and scalable data collection. Our codes, layouts, and models will be released after the paper is accepted.
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