论文/arXiv 2610.12046·20 天前

SkillWeave：将异构示教编织为长时程操作技能

SkillWeave结合遥操作与动觉示教，并以掩码条件扩散策略和终端引导，在三项真实长时程任务验证灵巧操作。

AI 整理

• 数据与采集
• 具身模型
• 端侧部署

原文链接：SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills（arXiv 2610.12046 · 发布日期：2026-10-08）

SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills

SkillWeave结合遥操作与动觉示教，并以掩码条件扩散策略和终端引导，在三项真实长时程任务验证灵巧操作。

核心要点

• 灵巧操作兼需大规模任务推进与精细接触交互，示教采集难以同时覆盖两类需求。
• 框架用遥操作处理粗到达和搬运，用动觉示教采集精细接触技能。
• 提出物体掩码条件扩散策略，并用后继感知终端引导缓解交接分布偏移。
• 三项真实任务成功率平均27%，子任务65%、交接87%，并公开视频和代码。

验证范围与局限：验证范围为三项真实世界长时程任务。

英文摘要 (Abstract)

Dexterous manipulation requires both large-scale task progression and precise contact-rich interaction, making it challenging to collect demonstrations that effectively support both regimes. We present SkillWeave, a heterogeneous demonstration framework for long-horizon dexterous manipulation that combines teleoperation for coarse reaching and transport with kinesthetic teaching for precise, contact-rich skills. To address the visual mismatch introduced by the demonstrator's presence during kinesthetic data collection, we propose an object-mask-conditioned diffusion policy that uses offline object segmentation for training supervision and a lightweight learned mask predictor at deployment, avoiding online segmentation and image inpainting. To mitigate distribution shift between independently trained sub-task policies, we introduce successor-aware terminal steering, which selects among actions sampled from the predecessor policy to guide the system toward states supported by the successor's demonstrated initial-state distribution. Across three real-world long-horizon tasks, SkillWeave achieves 27% average end-to-end success. Mask-conditioned kinesthetic policies improve dexterous sub-task success to an average of 65%, while successor-aware handoffs achieve an average composition efficiency of 87%. These results show that matching demonstration modality to interaction regime, explicitly addressing kinesthetic visual mismatch, and steering policy handoffs toward successor-supported states substantially improves long-horizon dexterous manipulation. Videos and code are available at skillweave-authors.github.io .

作者：Ryosei Tamura, Xiaoxiang Dong, Uksang Yoo, Yuemin Mao, Romina Mir, Jonathan Francis, Jeffrey Ichnowski

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.12046：SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills访问 2026-10-21