Assistance systems and reciprocal learning
Digitization and automation and the associated increasing use of assistance systems allow value creation to be increased in manufacturing and surrounding areas. While the fear of the replacement of human labor by machines and autonomous systems that accompanies the use of these technologies is widely discussed publicly and scientifically, the aspect of the further development of human skills in the environment of highly automated work environments hardly receives any attention.
In order to be able to use the new technological potentials of automation and digitization accordingly, it is important to develop human abilities, competencies, skills and qualifications accordingly. The majority of approaches in this area focus on unilateral learning (machine learning from machine or human) instead of bilateral learning in a hybrid constellation (simultaneous occurrence of human-machine learning). Reciprocal learning (also referred to as mutual learning) aims to simultaneously improve the capabilities of autonomous systems and the humans who work collaboratively with those systems.
In the research focus on assistance systems and reciprocal learning, the following questions will be addressed in particular:
- Under what circumstances does the use of assistance systems make sense?
- What strategies in the implementation of assistance systems are useful and what type of assistance (physical, cognitive, organizational, communication) is appropriate?
- Aspects of human-machine interaction and the design of human-machine interfaces?
- How can the use of assistance systems in production and assembly promote, control and support learning processes close to the workplace?
- How can individual assistance and learning needs be addressed?
- Evaluation and assessment of assistance systems
These questions and corresponding solutions are continuously supplemented, expanded and advanced on the basis of student theses, dissertations, research and industrial projects and pilot implementations.