Towards learning from demonstration system for parts assembly: A graph based representation for knowledge

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.