论文标题
通过抓地力对象的动态表示，学习高点到达和抓地力
Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction
论文作者
论文摘要
我们通过学习深入的增强学习的学习联合掌握和运动计划来解决高点伸出和抓紧的问题。为了在学习灵巧抓握的高维和复杂控制方面解决样本效率问题，我们提出了表征抓地力与目标对象之间空间相互作用的有效抓握状态的有效表示。为了表示抓地力对象的相互作用，我们采用了相互作用的双压表面（IBS），这是两个通过3D几何对象闭合的voronoi图，并已成功地用于表征3D对象之间的空间关系。我们发现，作为国家表示，IBS非常有效，因为它很好地为每只手指的细粒度控制具有针对目标对象的空间关系。这种新颖的掌握表示形式，以及几项技术贡献，包括快速IBS近似，基于矢量的新奖励和有效的训练策略，促进了学习强大掌握的强大控制模型，并具有良好的样本效率，动态适应性和跨类别的通用性。实验表明，它会产生高质量的灵巧掌握，并具有光滑的抓握运动的复杂形状。
We approach the problem of high-DOF reaching-and-grasping via learning joint planning of grasp and motion with deep reinforcement learning. To resolve the sample efficiency issue in learning the high-dimensional and complex control of dexterous grasping, we propose an effective representation of grasping state characterizing the spatial interaction between the gripper and the target object. To represent gripper-object interaction, we adopt Interaction Bisector Surface (IBS) which is the Voronoi diagram between two close by 3D geometric objects and has been successfully applied in characterizing spatial relations between 3D objects. We found that IBS is surprisingly effective as a state representation since it well informs the fine-grained control of each finger with spatial relation against the target object. This novel grasp representation, together with several technical contributions including a fast IBS approximation, a novel vector-based reward and an effective training strategy, facilitate learning a strong control model of high-DOF grasping with good sample efficiency, dynamic adaptability, and cross-category generality. Experiments show that it generates high-quality dexterous grasp for complex shapes with smooth grasping motions.
