Many robotic applications require to grasp objects not arbitrarily but at a very specific object part. This is especially important for manipulation tasks beyond simple pick-and-place scenarios or in robot-human interactions, such as object handovers. We propose AnyPart, a practical system that combines open-vocabulary object detection, open-vocabulary part segmentation and 6DOF grasp pose prediction to infer a grasp pose on a specific part of an object in 800 milliseconds. We contribute two new datasets for the task of open-vocabulary part-based grasping, a hand-segmented dataset containing 1014 object-part segmentations, and a dataset of real-world scenarios gathered during our robot trials for individual objects and table-clearing tasks. We evaluate AnyPart on a mobile manipulator robot using a set of 28 common household objects over 360 grasping trials. AnyPart is capable of producing successful grasps 69.52 %, when ignoring robot-based grasp failures, AnyPart predicts a grasp location on the correct part 88.57 % of the time.

翻译：许多机器人应用需要抓取物体的特定部件，而非任意位置。这对于超越简单拾放场景的操作任务或人机交互（如物体交接）尤为重要。我们提出AnyPart，一个实用系统，它结合了开放词汇目标检测、开放词汇部件分割和六自由度抓取姿态预测，可在800毫秒内推断出物体特定部件上的抓取姿态。我们为开放词汇部件级抓取任务贡献了两个新数据集：一个包含1014个物体-部件分割的手工标注数据集，以及在机器人试验中针对单个物体和桌面清理任务采集的真实场景数据集。我们在移动操纵机器人上使用28种常见家用物体进行了360次抓取试验来评估AnyPart。忽略机器人自身抓取失败的情况时，AnyPart能够以69.52%的成功率完成抓取，其中88.57%的抓取位置被预测在正确部件上。