Abstract
Effective execution of long-horizon tasks with dexterous robotic hands remains a significant challenge in real-world problems. While learning from human demonstrations have shown encouraging results, they require extensive data collection for training. Hence, decomposing long-horizon tasks into reusable primitive skills is a more efficient approach. To achieve so, we developed DexSkills, a novel supervised learning framework that addresses long-horizon dexterous manipulation tasks using primitive skills. DexSkills is trained to recognize and replicate a select set of skills using human demonstration data, which can then segment a demonstrated long-horizon dexterous manipulation task into a sequence of primitive skills to achieve one-shot execution by the robot directly. Significantly, DexSkills operates solely on proprioceptive and tactile data, i.e., haptic data. Our real-world robotic experiments show that DexSkills can accurately segment skills, thereby enabling autonomous robot execution of a diverse range of tasks.
Abstract (translated)
在现实问题中,使用灵巧的机器人手臂有效执行长期任务仍然是一个重要的挑战。虽然从人类演示中学习已经取得了鼓舞人心的结果,但它们需要大量的数据收集来进行训练。因此,将长期任务分解为可重复使用的原始技能是一种更有效的方法。为了实现这一点,我们开发了DexSkills,一种新颖的监督学习框架,它使用原始技能解决长期灵巧操作任务。DexSkills通过使用人类演示数据来识别和复制一系列技能,然后将演示的长期灵巧操作任务分割为一系列原始技能,使机器人可以直接实现一次性的任务。显著的是,DexSkills仅操作自适应和触觉数据,即触觉数据。我们在现实世界的机器人实验中证明,DexSkills可以准确地分割技能,从而使机器人能够自主执行各种任务。
URL
https://arxiv.org/abs/2405.03476
