Group 1 - The core concept of U-Arm is to address the pain points of traditional remote operation devices, which include high costs, low efficiency, and compatibility issues, by providing a high-performance, cost-effective, and open-source solution [1][4]. - U-Arm's core advantages can be summarized into four dimensions: stability, universality, cost-effectiveness, and openness [2][5]. Group 2 - U-Arm is designed specifically for embodied intelligence research and multi-scenario remote operation needs, breaking through traditional remote operation device limitations [4]. - The device features a dual-axis fixed joint design for stability, a lightweight yet impact-resistant body made of 4mm thick resin, and compatibility with 95% of commercial robotic arms [7][8]. Group 3 - U-Arm significantly reduces the initial investment required for remote operation solutions, priced at only 1999 yuan per unit, compared to traditional devices that can cost tens of thousands of dollars [17][18]. - The product includes a complete set of accessories, ensuring no hidden costs for users [8]. Group 4 - U-Arm's modular design allows for easy adaptation to various robotic arms, eliminating the need for separate remote operation devices for different arms [10][15]. - The device supports three core configurations that cover a wide range of mainstream robotic arms, ensuring compatibility and ease of use [11][12]. Group 5 - U-Arm enhances data collection efficiency by 39% compared to traditional methods, providing high-quality data for model training [11]. - The open-source nature of U-Arm allows for customization and supports educational practices, making it suitable for research and teaching [8][17]. Group 6 - The assembly process for U-Arm is straightforward, requiring no specialized technical skills, and includes clear steps for setup and initial operation [25][27]. - U-Arm provides a user-friendly experience, allowing beginners to quickly learn and operate the device with available examples and resources [28]. Group 7 - U-Arm offers a warranty for product quality, allowing for returns within two weeks for structural issues, ensuring customer satisfaction [29].

400元遥操95%机械臂，上海交大推出开源项目U-Arm！ 目前它已在XArm6、Dobot CR5、ARX R5等多种机械臂真机上进行了遥操作的验证。 △ 如何用更低的成本、更高的效率，去采集、复现和扩展人类的操作数据？ 遥操作是当前阶段的主流数据采集方案。 然而，完全同构的遥操作系统往往花费昂贵，例如ALOHA项目用两主两从完全同的机械臂进行遥操作，整套系统花费超过2万美金，而相对低成本的 VR、手柄、GELLO框架又存在奇异点、适配难等问题。 近日，来自上海交通大学的团队推出了一项开源解决方案——LeRobot-Anything-U-Arm。 这是一套仅需400元即可搭建、适配95%主流机械臂的通用遥操作系统。 3种结构覆盖市面主流机械臂类型 传统的主从遥操作系统通常要求主从臂严格同构，或以一个固定比例放缩几何尺度，这在直觉上确保人类操作者能够如预期地遥控从臂，但这在实践中并 非必要。 U-Arm重新设计了硬件方案，在压低成本的同时提升了可维护性和寿命。 团队指出，人类的视觉反馈可以自然地补偿硬件几何差异，只需保证关节的排布顺序一致，就能获得良好的操作体验。 而由于逆运动学解析解的存在性（Pieper准 ...

Core Viewpoint - Shanghai Jiao Tong University has launched an open-source remote operation project called U-Arm, which can be built for only 400 CNY and is compatible with 95% of mainstream robotic arms [4][3]. Cost Efficiency and System Design - Traditional remote operation systems are often expensive, with systems like the ALOHA project costing over 20,000 USD [2]. - U-Arm offers a low-cost solution that significantly reduces expenses while maintaining efficiency [4][15]. - The hardware design of U-Arm has been optimized to lower costs and improve maintainability and lifespan [14][15]. Compatibility and Usability - U-Arm is designed to work with three main structural configurations of robotic arms, allowing users to easily plug and play by selecting the appropriate hardware for their specific robotic arm type [8][16]. - The system has been validated on various robotic arms, including XArm6, Dobot CR5, and ARX R5 [10]. Performance and Efficiency - In experiments involving five different grasping tasks, U-Arm demonstrated a 39% reduction in average operation time compared to using a game controller [23]. - While U-Arm showed a decrease in success rates for precision tasks like can stacking, the overall efficiency gains in data collection were deemed acceptable trade-offs [24][23]. Data Quality and Natural Motion - U-Arm is capable of producing more natural motion trajectories compared to traditional controllers, which aids in better model convergence during training [25][27]. - The project has made all hardware and software resources available on GitHub, promoting further development and collaboration in the field [27].