What Do Humans and Machines Have in Common? That's Right, Empathy!

The special thing about interpersonal relationships is empathy, i.e. the ability to recognize and respond to the feelings and motives of others. What if cooperation between humans and machines were also characterized by these qualities? If the machine recognizes that the human is stressed and the human in turn knows the state of the machine at all times. Seven Fraunhofer Institutes are researching this synergetic collaboration in the “EMOTION” lighthouse project. 

The Fraunhofer flagship project “EMOTION” aims to introduce a new concept into production: “empathic systems”. In resilient production systems that are capable of reacting, learning and adapting, humans and intelligent machines must work hand in hand rather than side by side. However, this collaboration requires a completely new understanding of each other‘s abilities, goals and constitution, making empathy the decisive ability to make production systems more resilient and therefore future-proof. The seven participating Fraunhofer Institutes see empathy as the necessary core competence, in contrast to conventional cognitive systems. Empathic technical systems have the system network to independently incorporate and process information. Empathic systems are therefore an extension of conventional cognitive systems. In concrete terms, this should mean greater transparency in production, faster detection of deviations, better predictive and adaptive capabilities and, ultimately, cooperative learning. The aim of the project is to develop a reference model for emphatic production systems, taking into account the interoperability and individuality of the players, and to implement this technically by combining hardware and software solutions. In concrete terms, this means that the added value of human-technology solutions in terms of the responsiveness, learning and adaptability of production systems will be demonstrated and demonstrated through industry-oriented testing in the fields of assistance systems, maintenance and production planning and control. The target group includes users in plant, machine and vehicle construction as well as system manufacturers and system integrators in the digital economy. A core task lies in the design of a decentralized system architecture that ultimately leads to the sovereignty of the players. To achieve this, an infrastructure for digital factory management must first be created, with edge computing solutions and the necessary data protection training. A digital twin is then placed on top of this, which maps all the information of the stakeholders and production. The ultimate goal is to transfer the emphatic cooperation between two people to cooperation with and between machines, so that machines are informed of the other machine‘s status and goals and can then resolve a conflict independently. Such a socio-technical system can react much more flexibly to everyday logistical problems, such as machine malfunctions, product variant diversity, fluctuating demand and delivery bottlenecks. Digital networking, collective learning and cooperative decision-making mechanisms create individual cooperation between humans and assistance systems and machine-machine systems, which increases resilience enormously. Additional components and new sensors are currently being added to the system to make human-machine interaction even smoother and to obtain further data, such as on necessary maintenance tasks. All of this continues to serve the goal of allowing people and technology to react to each other in an agile manner, thereby strengthening the overall system.