Teleoperation systems are essential for efficiently collecting diverse and high-quality robot demonstration data, especially for complex, contact-rich tasks. However, current teleoperation platforms typically lack integrated force feedback, cross-embodiment generalization, and portable, user-friendly designs, limiting their practical deployment. To address these limitations, we introduce ACE-F, a cross embodiment foldable teleoperation system with integrated force feedback. Our approach leverages inverse kinematics (IK) combined with a carefully designed human-robot interface (HRI), enabling users to capture precise and high-quality demonstrations effortlessly. We further propose a generalized soft-controller pipeline integrating PD control and inverse dynamics to ensure robot safety and precise motion control across diverse robotic embodiments. Critically, to achieve cross-embodiment generalization of force feedback without additional sensors, we innovatively interpret end-effector positional deviations as virtual force signals, which enhance data collection and enable applications in imitation learning. Extensive teleoperation experiments confirm that ACE-F significantly simplifies the control of various robot embodiments, making dexterous manipulation tasks as intuitive as operating a computer mouse. The system is open-sourced at: https://acefoldable.github.io/

翻译：遥操作系统对于高效收集多样化和高质量的机器人演示数据至关重要，尤其是在处理复杂、接触密集的任务时。然而，当前的遥操作平台通常缺乏集成的力反馈、跨形态泛化能力以及便携、用户友好的设计，这限制了其实际部署。为解决这些局限性，我们提出了ACE-F，一种具有集成力反馈的跨形态可折叠遥操作系统。我们的方法利用逆运动学（IK）结合精心设计的人机交互界面（HRI），使用户能够轻松捕获精确且高质量的演示。我们进一步提出了一种集成PD控制和逆动力学的通用软控制器管道，以确保跨不同机器人形态的机器人安全性和精确运动控制。关键的是，为了实现无需额外传感器的力反馈跨形态泛化，我们创新性地将末端执行器的位置偏差解释为虚拟力信号，这增强了数据收集能力，并支持在模仿学习中的应用。广泛的遥操作实验证实，ACE-F显著简化了各种机器人形态的控制，使灵巧操作任务变得如同操作计算机鼠标一样直观。该系统已在 https://acefoldable.github.io/ 开源。