Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization-猫眼课题宝

DOI：

10.1016/j.robot.2020.103692

发表时间：

2020-11

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Yongqiang Huang;Juan Wilches;Yu Sun

浇注是人类日常生活中最常执行的任务之一，其精度受多种因素影响，包括要浇注的材料类型以及源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，学习浇注动力学，浇注运动，并从无监督的示范准确浇注的结果。学习浇注模型，然后推广到不同的条件下，如使用不习惯的浇注杯的自我监督实践。我们首先使用来自训练集的一个容器和四个新的但类似的容器来评估所提出的方法。所提出的方法实现了比普通人更好的倾倒准确性，并且所有五个杯子的倾倒速度相似。精度和浇注速度均优于最先进的产品。我们还使用与训练集中的容器大不相同的不习惯容器来评估所提出的自监督泛化方法。自我监督的泛化将不习惯的容器的倾倒误差降低到期望的精度水平。

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.

Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization

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