  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. However, learning such behaviors from scratch can be data
inefficient. To circumvent this, we propose a novel approach, DEFT (DExterous
Fine-Tuning for Hand Policies), that leverages human-driven priors, which are
executed directly in the real world. In order to improve upon these priors,
DEFT involves an efficient online optimization procedure. With the integration
of human-based learning and online fine-tuning, coupled with a soft robotic
hand, DEFT demonstrates success across various tasks, establishing a robust,
data-efficient pathway toward general dexterous manipulation. Please see our
website at https://dexterous-finetuning.github.io for video results.
