# SkillWeave：将异构示教编织为长时程操作技能

> 原文链接：https://robotics.hellobike.com/papers/2026-10-08-2610-12046
> 频道：papers | 更新日期：2026-10-08 | 标签：arxiv, cs.RO
> 核心摘要：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。

# SkillWeave：将异构示教编织为长时程操作技能

::: v-pre
> 原文链接：[SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills](https://arxiv.org/abs/2610.12046)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2610.12046：SkillWeave: Weaving Heterogeneous Demonstrations into Long-Horizon Manipulation Skills](https://arxiv.org/abs/2610.12046)