论文标题
从人类掌握的负担中学习可推广的灵巧操纵
Learning Generalizable Dexterous Manipulation from Human Grasp Affordance
论文作者
论文摘要
具有多指手的灵巧操作是机器人技术中最具挑战性的问题之一。尽管与强化学习相比，模仿学习的最新进展在很大程度上提高了样本效率，但考虑到有限的专家演示，学到的政策几乎无法概括以操纵新物体。在本文中，我们建议使用与人类掌握模型产生的大型3D对象的大规模演示学习灵巧的操作。这将政策概括为同一类别中的新颖对象实例。为了培训该政策，我们提出了一个新颖的模仿学习目标，并使用我们的演示进行了几何表示学习目标。通过在模拟中重新定位各种对象，我们表明我们的方法在操纵新物体时的表现优于基线。我们还消除了对操纵的3D对象表示学习的重要性。我们在项目网站-https：//kristery.github.io/ilad/上包括视频，代码和其他信息。
Dexterous manipulation with a multi-finger hand is one of the most challenging problems in robotics. While recent progress in imitation learning has largely improved the sample efficiency compared to Reinforcement Learning, the learned policy can hardly generalize to manipulate novel objects, given limited expert demonstrations. In this paper, we propose to learn dexterous manipulation using large-scale demonstrations with diverse 3D objects in a category, which are generated from a human grasp affordance model. This generalizes the policy to novel object instances within the same category. To train the policy, we propose a novel imitation learning objective jointly with a geometric representation learning objective using our demonstrations. By experimenting with relocating diverse objects in simulation, we show that our approach outperforms baselines with a large margin when manipulating novel objects. We also ablate the importance on 3D object representation learning for manipulation. We include videos, code, and additional information on the project website - https://kristery.github.io/ILAD/ .
