As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

中文翻译：

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具有不确定信息的对象的特征感应和机器人抓取：综述。

随着智能机器人的更多应用，它们的任务对象变得越来越多样化。但是，机器人处理不熟悉的物体仍然是一个挑战。我们回顾了有关具有不确定信息的物体的特征感测和机器人抓取的最新工作。特别是，我们专注于机器人如何感知对象的特征，以减少对象的不确定性，以及当传统方法失败时，机器人如何通过基于学习的方法完成对象的抓取。不确定信息分为几何信息和物理信息。根据不确定信息的类型，将对象进一步分为三类，即几何不确定对象，物理不确定对象和未知对象。此外，基于每种物体的变化特征，提出了这些物体的特征感知和机器人抓取方法。最后，我们总结了不确定对象的审查方法，并提供了一些有趣的问题，将来有待进一步研究。结果发现，物体的材质，紧凑性等特征难以感知，当任务物体的未知度增加时，基于学习网络的物体抓握方法起着更为重要的作用。