A data-driven framework decomposes manipulation tasks into reusable atomic skills, fine-tunes a VLA model per skill, and reports reduced data needs with comparable or better real-robot success rates.
A review of robot learning for manipu- lation: Challenges, representations, and algorithms
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An Atomic Skill Library Construction Method for Data-Efficient Embodied Manipulation
A data-driven framework decomposes manipulation tasks into reusable atomic skills, fine-tunes a VLA model per skill, and reports reduced data needs with comparable or better real-robot success rates.