Title: Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schrödinger Bridges Show Chinese title

Title: Grasp2Grasp：基于Schrödinger桥梁的灵巧抓取视觉转换

Abstract: We propose a new approach to vision-based dexterous grasp translation, which aims to transfer grasp intent across robotic hands with differing morphologies. Given a visual observation of a source hand grasping an object, our goal is to synthesize a functionally equivalent grasp for a target hand without requiring paired demonstrations or hand-specific simulations. We frame this problem as a stochastic transport between grasp distributions using the Schr\"odinger Bridge formalism. Our method learns to map between source and target latent grasp spaces via score and flow matching, conditioned on visual observations. To guide this translation, we introduce physics-informed cost functions that encode alignment in base pose, contact maps, wrench space, and manipulability. Experiments across diverse hand-object pairs demonstrate our approach generates stable, physically grounded grasps with strong generalization. This work enables semantic grasp transfer for heterogeneous manipulators and bridges vision-based grasping with probabilistic generative modeling. Show Chinese abstract

Abstract: 我们提出了一种新的基于视觉的灵巧抓取迁移方法，旨在跨具有不同形态的机器人手之间传递抓取意图。 给定源手抓取物体的视觉观察，我们的目标是在不需要配对手动演示或手特定模拟的情况下，为目标手合成功能等效的抓取方式。我们将该问题表述为使用薛定谔桥公式化方法的抓取分布之间的随机传输问题。 我们的方法通过基于分数匹配和流匹配学习在源和目标潜在抓取空间之间进行映射，并以视觉观察为条件。为了指导这种迁移，我们引入了物理信息成本函数，这些函数编码了基础姿态、接触图、力螺栓空间和可操作性的对齐信息。 多样化的手-物对实验表明，我们的方法能够生成稳定且物理上有依据的抓取方式，并具备强大的泛化能力。这项工作实现了异构操作器的语义抓取迁移，并将基于视觉的抓取与概率生成建模联系起来。