ACE-F: 一种具有力反馈的跨体折叠系统用于灵巧远程操作
ACE-F: A Cross Embodiment Foldable System with Force Feedback for Dexterous Teleoperation
AI总结:
ACE-F是一种具有力反馈的跨体折叠远程操作系统,通过逆运动学和软控制器流水线实现跨体泛化和精确控制,提升机器人灵巧操作的直观性和效率。
摘要:
远程操作系统对于高效收集多样且高质量的机器人演示数据至关重要,特别是对于复杂的、接触丰富的任务。然而,当前的远程操作平台通常缺乏集成的力反馈、跨体泛化和便携、用户友好的设计,限制了其实际部署。为了解决这些限制,我们引入了ACE-F,一种具有集成力反馈的跨体折叠远程操作系统。我们的方法利用逆运动学(IK)结合精心设计的人机交互(HRI)接口,使用户能够轻松捕捉精确且高质量的演示。我们进一步提出了一种通用的软控制器流水线,整合PD控制和逆动力学,以确保在各种机器人主体上实现机器人安全和精确运动控制。关键的是,为了在不增加额外传感器的情况下实现跨体力反馈泛化,我们创新性地将末端执行器位置偏差解释为虚拟力信号,这增强了数据收集并使在模仿学习中的应用成为可能。广泛的远程操作实验证实,ACE-F显著简化了各种机器人主体的控制,使灵巧操作任务变得像操作计算机鼠标一样直观。该系统在:https://acefoldable.github.io/上开源。
英文摘要:
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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