BooST: Bridging Semantics and Motions for Efficient Skill Transfer
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 ad...
| Authors | Jusuk Lee, Daesol Cho, Jonghun Shin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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