SLEM Project

Goals of SLEM

In the project "Self-learning and self-explanatory machine" an intelligent assistance system is developed, which supports users of different expertise adaptively, both in the operation of (special) machines, as well as the guidance of special production processes of complex products. In the following, operation includes maintenance, repair, retrofitting and installation of the machines. For this purpose, state data of the machines and interaction data of the user are recorded with the help of built-in sensors, which is then used for the AI training. In this way, the self-learning and self-explanatory machine can observe the trained user and learn from this how it should be operated. As a result, it will be able to identify the user's information needs and guide them appropriately through complex processes.


The status data of the machines, e.g. temperature curves or specific movement states of robot arms, can often already be recorded by machine-internal sensors and read out externally. Interaction data from the user can be recorded by inputs at the user interfaces such as a touch display or directly via gestures for operation or also recognition of activities around the machine, e.g. opening, filling. The fusion of all available data with additional synthetically generated data enables a detailed observation of the processes.

Use cases

  • Special machinery maintenance
  • Operation of series machines by untrained workers
  • Production processes of complex products at machine-based workstations

Added value

  • Less training required
  • Improved overall plant quality
  • Higher quality of operating results and avoidance of rejects due to error prevention
  • Faster reaction times to unforeseeable situations
  • Increased competence of flexibly deployable employees
  • Higher product acceptance by users due to increased ease of operation
  • Time savings through faster operation
  • Customer loyalty through improved maintenance offer
  • Increase in work safety
  • Adaptation to new operating situations


The SLEM project is funded by the Baden-Württemberg Ministry of Economics, Labor and Housing as part of the Baden-Württemberg AI Innovation Competition.