吸盘跳跃手：多指手上的吸盘实现具身灵巧性与手内遥操作
Suction Leap-Hand: Suction Cups on a Multi-fingered Hand Enable Embodied Dexterity and In-Hand Teleoperation
AI总结：
提出吸盘跳跃手（SLeap Hand），用指尖吸盘替代力闭合抓取，简化手内遥操作与数据收集，并实现单手剪纸、手内书写等超越人手的灵巧技能。
摘要：
灵巧的手内操作仍然是机器人学中的一个基础性挑战，其进展常常受到模仿人手这一主流范式的限制。这种拟人化方法造成了两个关键障碍：1）它将机器人的能力限制在人类已经能够完成的任务上；2）它使得为基于学习的方法收集数据变得极其困难。这两个挑战都是由传统的力闭合引起的，力闭合需要基于摩擦力、法向力和重力协调复杂的多点接触来抓取物体。这使得遥操作演示不稳定，并加剧了强化学习的仿真到现实差距。在这项工作中，我们提出了一种范式转变：从复制人类力学转向设计新颖的机器人形态。我们引入了吸盘跳跃手（SLeap Hand），这是一种多指手，其指尖集成了吸盘，实现了一种新的吸盘赋能的灵巧性。通过用稳定的单点粘附取代复杂的力闭合抓取，我们的设计从根本上简化了手内遥操作，并促进了高质量演示数据的收集。更重要的是，这种基于吸盘的形态解锁了一类新的灵巧技能，这些技能对于人手来说困难甚至不可能完成，例如单手剪纸和手内书写。我们的工作表明，通过超越拟人化约束，新颖的形态不仅能降低收集稳健操作数据的门槛，还能实现通常需要两只人手的任务的稳定单手完成。我们的网页是 https://sites.google.com/view/sleaphand。
英文摘要：
Dexterous in-hand manipulation remains a foundational challenge in robotics, with progress often constrained by the prevailing paradigm of imitating the human hand. This anthropomorphic approach creates two critical barriers: 1) it limits robotic capabilities to tasks humans can already perform, and 2) it makes data collection for learning-based methods exceedingly difficult. Both challenges are caused by traditional force-closure which requires coordinating complex, multi-point contacts based on friction, normal force, and gravity to grasp an object. This makes teleoperated demonstrations unstable and amplifies the sim-to-real gap for reinforcement learning. In this work, we propose a paradigm shift: moving away from replicating human mechanics toward the design of novel robotic embodiments. We introduce the \textbf{S}uction \textbf{Leap}-Hand (SLeap Hand), a multi-fingered hand featuring integrated fingertip suction cups that realize a new form of suction-enabled dexterity. By replacing complex force-closure grasps with stable, single-point adhesion, our design fundamentally simplifies in-hand teleoperation and facilitates the collection of high-quality demonstration data. More importantly, this suction-based embodiment unlocks a new class of dexterous skills that are difficult or even impossible for the human hand, such as one-handed paper cutting and in-hand writing. Our work demonstrates that by moving beyond anthropomorphic constraints, novel embodiments can not only lower the barrier for collecting robust manipulation data but also enable the stable, single-handed completion of tasks that would typically require two human hands. Our webpage is https://sites.google.com/view/sleaphand.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
