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Dexterity is often seen as a cornerstone of complex manipulation. Humans areable to perform a host of skills with their hands, from making food tooperating tools. In this paper, we investigate these challenges, especially inthe case of soft, deformable objects as well as complex, relativelylong-horizon tasks. However, learning such behaviors from scratch can be datainefficient. To circumvent this, we propose a novel approach, DEFT (DExterousFine-Tuning for Hand Policies), that leverages human-driven priors, which areexecuted directly in the real world. In order to improve upon these priors,DEFT involves an efficient online optimization procedure. With the integrationof human-based learning and online fine-tuning, coupled with a soft robotichand, DEFT demonstrates success across various tasks, establishing a robust,data-efficient pathway toward general dexterous manipulation. Please see ourwebsite at https://dexterous-finetuning.github.io for video results.

DEFT: Dexterous Fine-Tuning for Real-World Hand Policies