论文标题
Dexpoint：用于SIM到真实灵活性操纵的可推广点云增强学习
DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation
论文作者
论文摘要
我们为灵巧的操作提出了一个SIM到现实的框架，该框架可以推广到现实世界中同一类别的新对象。我们框架的关键是用点云输入和灵巧的手训练操纵政策。我们提出了两种新技术，以实现多个对象的联合学习和SIM到真实的概括：（i）将想象中的手点云作为增强输入； （ii）设计新颖的基于接触的奖励。我们使用Allegro手凭经验评估我们的方法，以掌握模拟和现实世界中的新物体。据我们所知，这是第一个基于政策学习的框架，它通过灵巧的手实现了这种概括结果。我们的项目页面可在https://yzqin.github.io/dexpoint上找到
We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge, this is the first policy learning-based framework that achieves such generalization results with dexterous hands. Our project page is available at https://yzqin.github.io/dexpoint
