论文/arXiv 2610.17789·15 天前

DexStitch：通过接触感知机器人修复从人类视频学习并组合灵巧技能

DexStitch将人类长程视频切分并转成机器人数据，学习可拼接灵巧技能，在留出会话和四个家庭技能上验证。

AI 整理

• 数据与采集

原文链接：DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting（arXiv 2610.17789 · 发布日期：2026-10-13）

DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting

DexStitch将人类长程视频切分并转成机器人数据，学习可拼接灵巧技能，在留出会话和四个家庭技能上验证。

核心要点

• 长程操作由基元技能及其转移组成，顺序执行需要两类监督，在机器人上采集成本高。
• 框架先用技能分类器分割人类视频，再转换为对齐的机器人观察、动作和技能标签数据。
• 逐指人-物接触指导灵巧手重定向与机器人修复，以保留接触几何和手指-物体遮挡顺序。
• 技能条件共享策略经合成数据预训练并用少量单技能遥操作微调，单技能成功率80.0%、序列成功率77.5%。

验证范围与局限：验证范围包括留出会话的分类一致性，以及四个家庭技能上的单技能和序列执行。

英文摘要 (Abstract)

A long-horizon manipulation task consists of primitive skills and the transitions connecting them. Learning skills that can be executed in sequence requires supervision for both, which is costly to collect on a robot. Human videos offer a scalable source of demonstrations, but transferring them to robots requires extracting robot-compatible observations and actions together with their skill structure. We present DexStitch, a framework that cuts long-horizon human videos into skills and transitions, converts them into aligned robot observation-action-skill data, and stitches the learned skills into a requested sequence. A skill classifier first segments the videos into manipulation skills and transitions. DexStitch then converts the human demonstrations into robot observations and actions while retaining their corresponding skill labels. During conversion, per-finger human-object contacts guide dexterous hand retargeting and robotic inpainting, preserving contact geometry and finger-object occlusion ordering. To stitch the learned skills into a sequence, a shared skill-conditioned policy is pretrained on the resulting synthetic data and finetuned on a mixture of synthetic data and a small set of single-skill teleoperation episodes. Conditioning on the current and next skills guides each transition toward the following skill, while predicted skill progression enables closed-loop switching. On a held-out session, the classifier agrees with manual annotations on 92.4% of frames. Across four household skills, DexStitch achieves 80.0% single-skill success and 77.5% sequence success.

作者：Byeonggyeol Choi, Seongmin Jung, Unghui Nam, Youngbo Song, Sunhong Min, Minhyung Lee, Byonghyo Shim, Yongseok Lee

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.17789：DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting访问 2026-10-21