DEFT：面向真实世界手部策略的灵巧微调
DEFT: Dexterous Fine-Tuning for Real-World Hand Policies
AI总结：
针对真实世界中柔软可变形物体操作、长周期复杂任务的灵巧操作数据效率低的问题，提出DEFT方法，结合人工先验与在线优化，搭配柔性机器人手实现多任务鲁棒高效的灵巧操作。
摘要：
灵巧性常被视为复杂操作的基石。人类能用双手完成从制作食物到操作工具的大量技能。本文研究了这些挑战，尤其是针对柔软可变形物体以及复杂、周期相对较长的任务的挑战。然而，从零开始学习这类行为数据效率极低。为规避这一问题，我们提出了一种新方法DEFT（手部策略灵巧微调），该方法利用直接在真实世界中执行的人工驱动先验。为优化这些先验，DEFT包含一套高效的在线优化流程。通过结合人工学习与在线微调，搭配柔性机器人手，DEFT在多种任务中均取得成功，为通用灵巧操作建立了一条鲁棒、数据高效的路径。视频结果详见我们的网站：https://dexterous-finetuning.github.io。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
