论文标题
机器人通过自学学习和概括获得准确的浇注技巧
Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization
论文作者
论文摘要
浇注是人类日常生活中最常见执行的任务之一，其准确性受多种因素的影响，包括要倒的材料类型以及源的几何形状和接收容器。在这项工作中，我们提出了一种自我监督的学习方法，可以从无监督的示范中学习倾泻动态，倾泻运动和结果，以进行准确的倾泻。然后，通过自制的练习来概括学到的浇注模型，例如使用不习惯的浇注杯子。我们首先使用训练组中的一个容器和四个新的但类似的容器评估了提议的方法。所提出的方法比普通人的浇注精度更好，所有五杯都具有类似的倒入速度。准确性和浇注速度都胜过最先进的功能。我们还使用不习惯的容器与训练集中的容器有很大不同的容器，评估了提出的自我监督的概括方法。自我监督的概括将未习惯的容器的倾泻误差降低到所需的精度水平。
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.
