Pouring is one of the most commonly executed tasks in humans' daily lives, whose accuracy is affected by multiple factors, including the type of material to be poured and the geometry of the source and receiving containers. In this work, we propose a self-supervised learning approach that learns the pouring dynamics, pouring motion, and outcomes from unsupervised demonstrations for accurate pouring. The learned pouring model is then generalized by self-supervised practicing to different conditions such as using unaccustomed pouring cups. We have evaluated the proposed approach first with one container from the training set and four new but similar containers. The proposed approach achieved better pouring accuracy than a regular human with a similar pouring speed for all five cups. Both the accuracy and pouring speed outperform state-of-the-art works. We have also evaluated the proposed self-supervised generalization approach using unaccustomed containers that are far different from the ones in the training set. The self-supervised generalization reduces the pouring error of the unaccustomed containers to the desired accuracy level.

中文翻译：

---

机器人通过自主学习和通用化获得准确的浇注技能

浇注是人类日常生活中最常执行的任务之一，其准确性受多个因素的影响，包括要浇注的物料类型以及源容器和接收容器的几何形状。在这项工作中，我们提出了一种自我监督的学习方法，该方法可以学习浇注动力学，浇注运动以及无人监督的演示结果，从而实现准确浇注。然后，通过自我监督的练习将学习的浇注模型推广到不同的条件，例如使用不习惯的浇注杯。我们首先用训练集中的一个容器和四个新的但相似的容器对提议的方法进行了评估。所提出的方法比五个杯子的倾倒速度都比普通人更好的倾倒精度。精度和浇注速度均优于最新技术。我们还使用与训练集中的容器相差甚远的非常规容器评估了拟议的自我监督泛化方法。自我监督的概括将不习惯的容器的倾倒误差降低到所需的精度水平。