可折叠力反馈系统让不同机器人操作像用鼠标一样简单
ACE-F: A Cross Embodiment Foldable System with Force Feedback for Dexterous Teleoperation
- 用逆运动学+人机交互设计，轻松捕捉高质量操作数据
- 无需额外传感器，通过末端位置偏差生成虚拟力信号
- 支持多种机器人形态通用，适合灵巧操作与模仿学习
遥操作系统对高效收集复杂、接触密集任务的多样化高质量机器人示范数据至关重要。然而，现有平台通常缺乏集成力反馈、跨形态泛化能力以及便携友好的设计，限制了实际部署。为此，我们提出ACE-F，一种集成力反馈的可折叠跨形态遥操作系统。通过结合逆运动学（IK）与精心设计的人机界面（HRI），用户可轻松捕捉精确且高质量的示范。我们进一步提出一种融合PD控制与逆动力学的通用软控制器流程，确保在多种机器人形态下实现安全精准运动控制。关键创新在于，为实现力反馈的跨形态泛化而无需额外传感器，我们创新性地将末端位置偏差解释为虚拟力信号，增强数据采集并支持模仿学习应用。大量遥操作实验表明，ACE-F显著简化了多种机器人形态的操作，使灵巧操作任务如同使用计算机鼠标般直观。系统已开源：https://acefoldable.github.io/
原文摘要 · Abstract (English)
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/
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