低成本力反馈遥操作平台，助力机器人模仿学习数据采集
Echo: An Open-Source, Low-Cost Teleoperation System with Force Feedback for Dataset Collection in Robot Learning
- 自研力反馈控制器，支持手柄与机械臂关节匹配
- 可调灵敏度模式，实现精准直观的双臂操作
- 开源硬件+代码，适合科研与初创团队快速复用
本文提出Echo，一种面向机器人模仿学习的数据集采集新型遥操作系统。系统专为UR机械臂设计，配备自研力反馈手柄和可调灵敏度模式，支持单手与双手操作，提升操控精度与直观性。集成友好的数据记录界面，简化高质量训练数据采集流程。系统具备高可靠性、低成本与可复现性，适用于研究机构、实验室及初创企业。当前适配UR机械臂，但架构可重构，未来可拓展至其他机械臂与人形机器人系统。通过系列实验验证其在复杂双臂任务中的有效性，显著加速机器人学习研究进程。开源资源包括装配说明、硬件文档与完整代码，详见https://eterwait.github.io/Echo/。
原文摘要 · Abstract (English)
In this article, we propose Echo, a novel joint-matching teleoperation system designed to enhance the collection of datasets for manual and bimanual tasks. Our system is specifically tailored for controlling the UR manipulator and features a custom controller with force feedback and adjustable sensitivity modes, enabling precise and intuitive operation. Additionally, Echo integrates a user-friendly dataset recording interface, simplifying the process of collecting high-quality training data for imitation learning. The system is designed to be reliable, cost-effective, and easily reproducible, making it an accessible tool for researchers, laboratories, and startups passionate about advancing robotics through imitation learning. Although the current implementation focuses on the UR manipulator, Echo architecture is reconfigurable and can be adapted to other manipulators and humanoid systems. We demonstrate the effectiveness of Echo through a series of experiments, showcasing its ability to perform complex bimanual tasks and its potential to accelerate research in the field. We provide assembly instructions, a hardware description, and code at https://eterwait.github.io/Echo/.
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