Grasping objects with limited or no prior knowledge about them is a highly relevant skill in assistive robotics. Still, in this general setting, it has remained an open problem, especially when it comes to only partial observability and versatile grasping with multi-fingered hands. We present a novel, fast, and high fidelity deep learning pipeline consisting of a shape completion module that is based on a single depth image, and followed by a grasp predictor that is based on the predicted object shape. The shape completion network is based on VQDIF and predicts spatial occupancy values at arbitrary query points. As grasp predictor, we use our two-stage architecture that first generates hand poses using an autoregressive model and then regresses finger joint configurations per pose. Critical factors turn out to be sufficient data realism and augmentation, as well as special attention to difficult cases during training. Experiments on a physical robot platform demonstrate successful grasping of a wide range of household objects based on a depth image from a single viewpoint. The whole pipeline is fast, taking only about 1 s for completing the object's shape (0.7 s) and generating 1000 grasps (0.3 s).

翻译：在辅助机器人领域，抓取先验知识有限或未知的物体是一项至关重要的技能。然而，在这一通用设定下，尤其是在仅存在部分可观测性且需使用多指手进行通用抓取时，该问题仍未得到解决。我们提出了一种新颖、快速且高保真的深度学习流程，该流程包含一个基于单张深度图像的形状补全模块，其后接一个基于预测物体形状的抓取预测器。形状补全网络基于VQDIF构建，可预测任意查询点处的空间占据值。作为抓取预测器，我们采用两阶段架构：首先通过自回归模型生成手部姿态，随后为每个姿态回归手指关节配置。关键因素包括足够的数据真实性与增强，以及在训练中对困难样本的特别关注。在实体机器人平台上的实验表明，基于单视角深度图像，系统能成功抓取多种家居物品。整个流程运行迅速，完成物体形状补全（0.7秒）并生成1000个抓取姿态（0.3秒）仅需约1秒。