Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture dynamic object shape features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and Local-Geo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method to learn a generalizable grasping policy DexRepNet. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93\% grasping success rate on seen objects and higher than 80\% grasping success rates on diverse objects of unseen categories in both simulation and real-world experiments.

翻译：机器人灵巧抓取因多指手的高自由度与复杂接触而极具挑战。现有基于深度强化学习的方法利用人类示教来降低高维动作空间下的样本复杂度。然而，针对高层泛化的手-物交互表征研究仍显不足。本文提出一种新颖的几何与空间手-物交互表征DexRep，用以捕获抓取过程中动态物体形状特征以及手与物体间的空间关系。DexRep包含：通过手部运动感知粗糙形状的占据特征、反映手-物表面距离变化的表面特征，以及与潜在接触最相关的局部几何表面特征。基于该表征，我们提出一种可泛化的灵巧抓取深度强化学习方法DexRepNet。实验结果表明，在抓取成功率和收敛速度上，该方法显著优于使用现有表征的基线方法。在仿真与真实实验中，该方法对已知物体的抓取成功率达93%，对未见类别多样物体的抓取成功率超过80%。