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.

翻译：摘要：灵巧操作常被视为复杂操控能力的基石。人类能借助双手完成从烹饪食物到操作工具等一系列技能。本文重点研究软质可变形物体及复杂长周期任务场景下的此类挑战。然而，从零开始学习此类行为往往存在数据效率低下的问题。为规避这一局限，我们提出一种新型方法DEFT（灵巧手部策略微调），该方法利用可直接在真实世界中执行的人类驱动先验知识。为优化这些先验知识，DEFT引入了高效的在线优化流程。通过融合基于人类的学习机制与在线微调技术，并结合软体机械手，DEFT在多项任务中展现出卓越性能，为通用灵巧操作开辟了一条稳健且数据高效的实现路径。视频演示结果请访问我们的网站：https://dexterous-finetuning.github.io。