Grasp2Grasp：基于视觉的灵巧抓取翻译，通过薛定谔桥实现
Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schrödinger Bridges
AI总结：
提出基于薛定谔桥的视觉抓取翻译方法，跨形态手间传递抓取意图，通过物理信息成本函数生成稳定泛化的抓取。
摘要：
我们提出了一种新的基于视觉的灵巧抓取翻译方法，旨在跨不同形态的机器人手传递抓取意图。给定源手抓取物体的视觉观察，我们的目标是为目标手合成功能等效的抓取，无需成对演示或特定于手的模拟。我们将此问题构建为使用薛定谔桥形式在抓取分布之间的随机传输。我们的方法通过分数和流匹配，在视觉观察条件下，学习源和目标潜在抓取空间之间的映射。为引导此翻译，我们引入了物理信息成本函数，编码基座姿态、接触图、力旋量空间和可操作性方面的对齐。跨多样手-物体对的实验表明，我们的方法生成稳定、物理基础的抓取，具有强泛化能力。这项工作实现了异构操作器的语义抓取传递，并将基于视觉的抓取与概率生成建模联系起来。更多细节见 https://grasp2grasp.github.io/
英文摘要：
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 Schrödinger 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. Additional details at https://grasp2grasp.github.io/
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。
