Use of Action Label in Deep Predictive Learning for Robot Manipulation

Kei Kase, Chikara Utsumi, Y. Domae, T. Ogata
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引用次数: 0

Abstract

Various forms of human knowledge can be explicitly used to enhance deep robot learning from demonstrations. Annotation of subtasks from task segmentation is one type of human symbolism and knowledge. Annotated subtasks can be referred to as action labels, which are more primitive symbols that can be building blocks for more complex human reasoning, like language instructions. However, action labels are not widely used to boost learning processes because of problems that include (1) real-time annotation for online manipulation, (2) temporal inconsistency by annotators, (3) difference in data characteristics of motor commands and action labels, and (4) annotation cost. To address these problems, we propose the Gated Action Motor Predictive Learning (GAMPL) framework to leverage action labels for improved performance. GAMPL has two modules to obtain soft action labels compatible with motor commands and to generate motion. In this study, GAMPL is evaluated for towel-folding manipulation tasks in a real environment with a six degrees-of-freedom (6 DoF) robot and shows improved generalizability with action labels.
动作标签在机器人操作深度预测学习中的应用
从演示中可以明确地使用各种形式的人类知识来增强深度机器人学习。任务分词对子任务的标注是一种人类符号和知识。带注释的子任务可以称为动作标签,它是更原始的符号,可以作为更复杂的人类推理(如语言指令)的构建块。然而,由于存在以下问题,动作标签并没有被广泛用于促进学习过程:(1)在线操作的实时注释,(2)注释者的时间不一致,(3)运动命令和动作标签的数据特征不同,以及(4)注释成本。为了解决这些问题,我们提出了门控动作运动预测学习(GAMPL)框架来利用动作标签来提高性能。GAMPL有两个模块来获得与电机命令兼容的软动作标签并生成运动。在这项研究中,GAMPL在一个六自由度(6 DoF)机器人的真实环境中对折叠毛巾的操作任务进行了评估,并通过动作标签显示出改进的泛化性。
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