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Hierarchical Skill Retrieval for Data-Efficient Adaptation of Vision-Language-Action Models
While Vision-Language-Action (VLA) models pretrained on large-scale robot datasets provide a strong foundation for robot manipulation, their performance can degrade when adapted to new tasks with limited task-specific demonstrations. Retrieval offers a practical way to reuse existing demonstrations for data-efficient adaptation, but existing methods often rely on visual similarity, state-action representations, or task-level language matching. These approaches may overlook the hierarchical structure of long-horizon manipulation tasks, where complete task matches are rare but reusable skills ar
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T04:04:49.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.