DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
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 (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.
BibTeX
@conference{Kannan-2023-138051,author = {Aditya Kannan and Kenneth Shaw and Shikhar Bahl and Pragna Mannam and Deepak Pathak},
title = {DEFT: Dexterous Fine-Tuning for Real-World Hand Policies},
booktitle = {Proceedings of (CoRL) Conference on Robot Learning},
year = {2023},
month = {August},
keywords = {Dexterous Manipulation, Reinforcement Learning, Learning from Videos},
}