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面临挑战。现有基于深度强化学习的方法通过利用人类示教来降低高维动作空间带来的样本复杂度。然而，针对高层级泛化的手物交互表示研究仍显不足。本文提出一种新型几何与空间手物交互表示——DexRep，用于捕捉抓取过程中动态物体形状特征及手物空间关系。DexRep包含：通过移动手部感知传感范围内粗糙形状的占用特征、反映手物表面距离变化的表面特征，以及提取与潜在接触最相关的局部几何曲面特征的局部几何特征。基于该表示，我们提出一种深度强化学习方法，构建可泛化的灵巧抓取策略网络DexRepNet。实验结果表明，该方法在抓取成功率和收敛速度上均显著优于基于现有表示的机器人抓取基线模型。在仿真与真实实验中，其对已知物体的抓取成功率达93%，对未见类别多样化物体的抓取成功率超过80%。