用手机实现低成本通用机器人操控，高效收集训练数据
Phone2Act: A Low-Cost, Hardware-Agnostic Teleoperation System for Scalable VLA Data Collection
- 手机+ARCore变6自由度控制器，兼容多种机器人平台
- 130条演示数据训练后，真实任务成功率90%
- 无需额外设备，适合资源有限的研究团队
为视觉-语言-动作（VLA）模型训练收集多样化、高质量的操作数据仍成本高昂，因现有遥操作框架依赖专用硬件或与特定机器人平台强绑定。我们提出Phone2Act，一种低成本、硬件无关的遥操作框架，通过Google ARCore将普通智能手机转化为6自由度机器人控制器。基于模块化ROS 2架构，Phone2Act通过可替换的桥接节点解耦控制逻辑与硬件细节，支持从工业协作机器人到低成本双臂机器人的多种平台，无需修改代码。通用记录器同步多摄像头RGB流与机器人状态反馈，并以LeRobot数据集格式原生导出，省去后期处理，可直接用于VLA微调。我们通过在130个采集的示范片段上微调GR00T-N1.5，在真实世界的多阶段抓取放置任务中实现了90%的成功率，部署于物理Dobot CR5机器人上。
原文摘要 · Abstract (English)
Collecting diverse, high-quality manipulation data for Vision-Language-Action (VLA) model training remains prohibitively expensive for many research groups, as existing teleoperation frameworks rely on specialized hardware or are tightly coupled to specific robot platforms. We present Phone2Act, a low-cost, hardware-agnostic teleoperation framework that transforms a commodity smartphone into a 6-DoF robot controller via Google ARCore. Built on a modular ROS 2 architecture, Phone2Act decouples control logic from hardware specifics through interchangeable bridge nodes, supporting platforms from industrial cobots to low-cost bimanual arms without code modification. A Universal Recorder synchronizes multi-camera RGB streams with robot state feedback and exports demonstrations natively in the LeRobot dataset format, eliminating post-processing and enabling immediate VLA fine-tuning. We validate the framework by fine-tuning GR00T-N1.5 on 130 collected episodes, achieving a 90% success rate on a real-world multi-stage pick-and-place task deployed on a physical Dobot CR5.
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