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标题: "BooST: Bridging Semantics and Motions for Efficient Skill Transfer"
原文链接: "https://arxiv.org/abs/2608.10600"
发布日期: "2026-08-11"
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## Computer Science > Robotics

## Title:BooST: Bridging Semantics and Motions for Efficient Skill Transfer

Authors:[Jusuk Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+J), [Daesol Cho](https://arxiv.org/search/cs?searchtype=author&query=Cho,+D), [Jonghun Shin](https://arxiv.org/search/cs?searchtype=author&query=Shin,+J), [Seungyeon Yoo](https://arxiv.org/search/cs?searchtype=author&query=Yoo,+S), [Jonghae Park](https://arxiv.org/search/cs?searchtype=author&query=Park,+J), [Taekbeom Lee](https://arxiv.org/search/cs?searchtype=author&query=Lee,+T), [H. Jin Kim](https://arxiv.org/search/cs?searchtype=author&query=Kim,+H+J)

[View PDF](https://arxiv.org/pdf/2608.10600) [HTML (experimental)](https://arxiv.org/html/2608.10600v1)

> Abstract:Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot learning. For efficient skill transfer to real robots, learned skills must generalize across tasks and domains, remain robust to visual and dynamic perturbations, and be efficient enough for practical deployment. However, existing methods typically satisfy only a subset of these properties, as they capture either high-level semantic intent (what) or low-level motion dynamics (how). This incomplete skill transfer yields weak priors for policy learning, thereby demanding substantial in-domain data for downstream adaptation. To address these challenges, we introduce BooST, a two-stage framework that explicitly bridges semantics and motions to satisfy all three desiderata. BooST first leverages a cross-modal VQ-VAE to capture both semantic intent and motion dynamics, yielding a unified skill representation. It then distills this representation into a lightweight policy for efficient downstream adaptation to new tasks. Extensive experiments across simulation and real-robot settings demonstrate that BooST achieves superior few-shot adaptation, cross-domain skill transfer, and robustness to dynamic visual distractors, while maintaining a lightweight yet expressive design suitable for real-world deployment.

| Comments: |  |
| --- | --- |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | [arXiv:2608.10600](https://arxiv.org/abs/2608.10600) \[cs.RO\] |
|  | (or [arXiv:2608.10600v1](https://arxiv.org/abs/2608.10600v1) \[cs.RO\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10600](https://doi.org/10.48550/arXiv.2608.10600) |

## Submission history

From: Jusuk Lee \[[view email](https://arxiv.org/show-email/879c97be/2608.10600)\]  
**\[v1\]** Tue, 11 Aug 2026 07:35:12 UTC (1,373 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10600) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
