上交发布U-Arm：突破成本壁垒，实现超低成本通用机械臂遥操作

Research Background and Core Needs - The bottleneck in dual robotic arm strategy learning is the lack of large-scale, high-quality real-world operational data, which is more applicable for training robust policies compared to simulation or purely human data. Currently, the main method for obtaining such data is through human demonstrations, necessitating a reliable teleoperation interface [4]. Existing Demonstration Interfaces - Existing demonstration interfaces are categorized into two types. U-ARM aims to resolve the conflict between "high compatibility" and "low cost" by creating an open-source, ultra-low-cost, and easily adaptable master-slave teleoperation system, enabling researchers to quickly set up data collection pipelines for various commercial robotic arms [5]. Pain Points of Existing Solutions and U-ARM's Positioning - Current mainstream teleoperation devices face issues such as kinematic singularities, workspace limitations, and insufficient precision, requiring complex post-processing. High-cost solutions like ALOHA and GELLO are effective but prohibitively expensive, with ALOHA costing over $50,000 and GELLO at $270. U-ARM fills the gap between ultra-low cost and high compatibility, with a single-arm cost of $50.5 for 6DoF and $56.8 for 7DoF, while ensuring usability without motion sickness and easy bimanual operation [6][9]. U-ARM System Design - U-ARM's hardware design is based on standardized configurations that most commercial 6/7 degree-of-freedom robotic arms follow. It offers three configurations to adapt to different commercial robotic arms, ensuring compatibility and cost optimization [10][14]. Mechanical Structure and Motor Modification - U-ARM's components are made from PLA 3D printing with a minimum wall thickness of 4mm for durability. The design addresses common issues in low-cost 3D printed arms by using a dual-axis fixation for joints to alleviate high radial loads. The motor modification involves removing the internal gearbox to reduce resistance, allowing for smoother movement while maintaining stability [13][16]. Algorithm Design - The algorithm ensures smooth motion and adaptability by calibrating the encoder and implementing filtering and interpolation techniques to avoid jitter during operation. This is crucial for maintaining the accuracy of the teleoperation system [16][17]. Experimental Validation and Results Analysis - The experiments included "simulation adaptation" and "real-world comparison" to validate U-ARM's adaptability and efficiency advantages. U-ARM was tested against Joycon in performing typical desktop tasks, showing a significant efficiency improvement of 39% in task completion time [18][24]. Efficiency and Success Rate - U-ARM achieved an average task completion time of 17.7 seconds with a success rate of 75.8%, while Joycon had an average time of 29.04 seconds and a success rate of 83%. The efficiency gain is attributed to U-ARM's design, which allows for more natural and rapid movements, despite a slight trade-off in precision during fine operations [24].