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首页> 外文会议>IEEE Annual International Conference on Cyber Technology in Automation, Control and Intelligent Systems >Towards learning from demonstration system for parts assembly: A graph based representation for knowledge
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Towards learning from demonstration system for parts assembly: A graph based representation for knowledge

机译：从零件装配演示系统中学习：基于图的知识表示

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The industrial robot plays an increasingly important role in manufacturing industry, but it is limited by its high prerequisite of programming on users, which is time consuming and challenging. In this context, a mechanism enabling autoprogram of robot is desired. Learning from demonstration system (LDS) is one of systems that aim on this goal. In this paper, we present a graph based representation of knowledge called assembly graph (AG) to describe the knowledge on parts assembly, which is independent on specific workspace or robot. Based on this graph representation, a LDS framework is then proposed for parts assembly to enable knowledge transform and knowledge transfer. The former means that the knowledge on part assembly can be transformed from the human to the robot and the latter means that the knowledge can be transferred from the leaned computer to any other robots with other workspace. Besides, we present an implementation of this conceptual framework consisting of two stages including demonstration and execution. In demonstration stage, a human teacher shows the process of parts assembly, which is recorded by a camera system. The assembly relations are detected from images and represented by AG. It is solved by taking property of parts as well as the robot into consideration to obtain the precise pose of each part. By using the solution, the robot can assemble the parts as shown in demonstration in the execution stage. In the experiment, the building blocks are used for assembly. Our robot succeeds in assembling some building blocks together which is initially demonstrated by a human teacher.
机译：工业机器人在制造业中扮演着越来越重要的角色，但是受限于其对用户进行编程的高先决条件，这既费时又具有挑战性。在这种情况下，需要一种能够对机器人进行自动编程的机制。从演示系统（LDS）中学习是针对该目标的系统之一。在本文中，我们提出了一种基于图形的知识表示形式，称为装配图（AG），以描述有关零件装配的知识，该知识独立于特定的工作区或机器人。然后，基于该图形表示，提出了用于零件装配的LDS框架，以实现知识转换和知识传递。前者意味着可以将零件组装方面的知识从人类转换为机器人，而后者意味着可以将知识从精益计算机上转移到具有其他工作空间的任何其他机器人上。此外，我们介绍了这个概念框架的实现，包括演示和执行两个阶段。在演示阶段，一位人类老师演示了零件组装的过程，该过程由摄像系统记录下来。从图像中检测装配关系，并用AG表示。通过考虑零件以及机器人的属性来解决，以获得每个零件的精确姿态。通过使用该解决方案，机器人可以在执行阶段按照演示中的说明组装零件。在实验中，使用了构建基块进行组装。我们的机器人成功地将一些构建块组装在一起，最初是由人类老师演示的。

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