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title
DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
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Efficient Real-World Fine-Tuning for Dexterous Control
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DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
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2026-02-07 20:45:50
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DEFT: Dexterous Fine-Tuning for Real-World Hand Policies DEFT: Dexterous Fine-Tuning for Real-World Hand Policies Aditya Kannan* Kenneth Shaw* Shikhar Bahl Pragna Mannam Deepak Pathak Carnegie Mellon University CoRL 2023 *denotes equal contribution CoRL Paper arXiv Poster Summary Grasp Data Method Video Abstract Dexterity is often seen as a cornerstone of complex manipulation. Humans are able to perform a host of skills with their hands, from making food to operating tools. In this paper, we investigate these challenges, especially in the case of soft, deformable objects as well as complex, relatively long-horizon tasks. Although, learning such behaviors from scratch can be data inefficient. To circumvent this, we propose a novel approach, DEFT ( DE xterous F ine- T uning for Hand Policies), that leverages human-dr...
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