Vi-TacMan：通过视觉和触觉进行关节物体操作
Vi-TacMan: Articulated Object Manipulation via Vision and Touch
AI总结：
本文提出Vi-TacMan，结合视觉和触觉反馈实现关节物体的精确操控，通过表面法线作为几何先验并利用von Mises-Fisher分布建模方向，显著优于基线方法，且无需显式运动学模型。
摘要：
自主操控关节物体仍是机器人在人类环境中面临的基本挑战。基于视觉的方法可以推断隐藏的运动学，但在不熟悉的物体上估计不够精确。触觉方法通过接触反馈实现稳健控制，但需要准确的初始化。这表明了一种自然的协同：视觉用于全局指导，触觉用于局部精度。然而，目前没有框架系统地利用这种互补性进行通用的关节操控。本文提出了Vi-TacMan，利用视觉提出抓取和粗略方向，为触觉控制器提供种子，以实现精确执行。通过将表面法线作为几何先验，并通过von Mises-Fisher分布建模方向，我们的方法在所有p<0.0001的情况下显著优于基线方法。关键的是，操控无需显式运动学模型——触觉控制器通过实时接触调节来细化粗略的视觉估计。在超过50,000个模拟和多样化的现实物体上的测试证实了稳健的跨类别泛化。这项工作证明了当与触觉反馈结合时，粗略的视觉提示足以实现可靠的操控，为无结构环境中的自主系统提供了一种可扩展的范式。
英文摘要：
Autonomous manipulation of articulated objects remains a fundamental challenge for robots in human environments. Vision-based methods can infer hidden kinematics but can yield imprecise estimates on unfamiliar objects. Tactile approaches achieve robust control through contact feedback but require accurate initialization. This suggests a natural synergy: vision for global guidance, touch for local precision. Yet no framework systematically exploits this complementarity for generalized articulated manipulation. Here we present Vi-TacMan, which uses vision to propose grasps and coarse directions that seed a tactile controller for precise execution. By incorporating surface normals as geometric priors and modeling directions via von Mises-Fisher distributions, our approach achieves significant gains over baselines (all p<0.0001). Critically, manipulation succeeds without explicit kinematic models -- the tactile controller refines coarse visual estimates through real-time contact regulation. Tests on more than 50,000 simulated and diverse real-world objects confirm robust cross-category generalization. This work establishes that coarse visual cues suffice for reliable manipulation when coupled with tactile feedback, offering a scalable paradigm for autonomous systems in unstructured environments.
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。
