Abstract
To maintain productivity and deal with mass customization, manufacturing industries worldwide are increasingly installing collaborative robots. However, the problem of easily interacting and teaching tasks to a collaborative robot is still unsolved. We present a programming-framework that exploits and integrates different programming paradigms such as Learning-by-demonstration (LbD), Learning-by-interaction (LbI) and Learning-by-programming (LbP) using a semantically-enhanced-reasoning-system. The framework combines their individual advantages and alleviate drawbacks when learning complex assembly processes (AP). First, the AP are abstracted at task level and the sequence of the tasks are learned using the LbD approach. The reasoning engine semantically links the tasks to the AP (allows knowledge portability). Then, LbP approach is exploited to program the relevant skills to the robot for task execution. During robot execution, uncertainties are solved by iterative interaction with the user using a GUI based LbI approach. The framework is evaluated on a real-world use case and demonstrates promising results.
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