Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory

Process automation is essential to establish an economically viable circular factory in high-wage locations. This involves using autonomous production technologies, such as robots, to disassemble, reprocess, and reassemble used products with unknown conditions into the original or a new generation of products. This is a complex and highly dynamic issue that involves a high degree of uncertainty. To adapt robots to these conditions, learning from humans is necessary. Humans are the most flexible resource in the circular factory and they can adapt their knowledge and skills to new tasks and changing conditions. This paper presents an interdisciplinary research framework for learning human action knowledge from complex manipulation tasks through human observation and demonstration. The acquired knowledge will be described in a machine-executable form and will be transferred to industrial automation execution by robots in a circular factory. There are two primary research objectives. First, we investigate the multi-modal capture of human behavior and the description of human action knowledge. Second, the reproduction and generalization of learned actions, such as disassembly and assembly actions on robots is studied.