The project Cognitively and Empathically Intelligent Collaborating Robots (KEIKO), funded by the MWK through the SPRUNG program since 2023, has been building on the HerMes project.

Increasing networking and digitization is permeating all areas of our industrial society. It enables new networked processes and systems as well as innovative applications, services and business models in industrial manufacturing, logistics and supply web management, energy management, transport and traffic or the life cycle management of complex products. A new type of socio-technical cyberphysical systems is emerging, in which software components, machines, means of transport and intelligent objects (workpieces, consignments) are actors that are increasingly equipped with autonomy and self-control. The comprehensive networking and coordination of these machine actors among themselves on the one hand and with humans on the other hand results in complex and dynamic overall systems whose development (engineering, modeling and simulation), validation and optimization are associated with major scientific challenges.

For the interdisciplinary scientific investigation of these questions against the background of the above-mentioned fields of application, the consideration of heterogeneous functional and organizational networks (in the following: Human-Machine-Teams (HMTs))) is of particular importance, consisting of networked human and machine actors. They coordinate actions in common environments to achieve local goals or are entrusted with the execution of common tasks. They exchange information and optimize their use of resources or task-related collaboration.

The mastery of heterogeneous human-machine teams requires interdisciplinary cooperation, especially between computer science, information and systems technology, mathematics, data science and economics. The research of models, methods and technologies for engineering, simulation, validation and optimization of heterogeneous HMTs forms the basis of future industrial production systems, in which humans together with automated conveyor and robot systems form teams for the realization of flexible and efficient manufacturing or dismantling.

An interdisciplinary team of researchers will investigate current issues in the context of heterogeneous HMTs:
 

1. Engineering and validation of heterogeneous HMTs: Which models and methods can be used to validate the behavior of heterogeneous HMTs? Concrete questions include:
    
   
   1. How must human-machine interfaces be designed to enable the safe transfer of control between human and machine actors?
       
   2. How can the human control capability (i.e. willingness or ability to take or maintain control) be reliably assessed before the transfer of control from a human to a machine or backwards?
       

2. Simulation and optimization of heterogeneous HMTs: With which methods and tools can heterogeneous HMTs be mapped in simulations under consideration of real system contexts? How can continuous monitoring, control and optimization of heterogeneous HMTs be achieved in combination with sensors, actuators and simulation? Concrete questions include:
    
   
   1. How can accurate contextual models of heterogeneous HMTs (e.g. realistic movement and intention models at the operational or tactical level) be provided using networked sensor technologies and used e.g. in simulations?
       
   2. Which mechanisms are suitable for modelling and optimizing coordination of heterogeneous HMTs? Which algorithms can be used for continuous optimization under consideration of temporal boundary conditions?

• Project 1.1: Safe ad-hoc cooperation between humans and autonomous machines
   
• Project 1.2: Sensor-based assessment of the ability of persons interacting with machines to take over the machine operation
   
  
• Project 2.1: Data assimilation for sensor-based modelling of movement patterns of human and machine actors
   
  
• Project 2.2: Modelling and optimization of the coordination of heterogeneous human-machine-teams

• S. Hossain et al. SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories. 2022. arXiv:2202.02791 [cs.RO].
   
• B. Alhaji, M. Prilla, and A. Rausch. “Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration”. In: Frontiers in Artificial Intelligence 4 (2021), p. 103.
   
• B. Alhaji, M. Prilla, and A. Rausch. “Trust, but Verify: Autonomous Robot Trust Modeling in Human-Robot Collaboration”. In: Proceedings of the 9th InternationalConference on Human-Agent Interaction. HAI ’21. New York, NY, USA: Association for Computing Machinery, Nov. 2021, pp. 402–406.
   
• F. Merz et al. “An Auction-based Mechanism for the Formation and Scheduling of Heterogeneous Human-machine Teams”. In: 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE. 2021, pp. 863–868.
   
• N.-O. Rohweder, J. Gertheiss, and C. Rembe. “Sub-micron pupillometry for optical EEG measurements”. In: tm-Technisches Messen 88.7-8 (2021), pp. 473–480.
   
• B. Alhaji, A. Rausch, and M. Prilla. “Toward Mutual Trust Modeling in Human-Robot Collaboration”. English. In: Proceedings of the Workshop on Respecting Human Autonomy through Human-Centered AI at NordiCHI 2020. Accepted. 2020, p. 4.
   
• B. Alhaji et al. “Engineering human–machine teams for trusted collaboration”. In: Big Data and Cognitive Computing 4.4 (2020), p. 35.
   
• F. Merz et al. “A Multi-Round Auction for Staff to Job Assignment Under MyopicBest Response Dynamics”. In: 2020 IEEE International Conference on IndustrialEngineering and Engineering Management (IEEM). IEEE. 2020, pp. 1137–1141.
   
• N.-O. Rohweder, C. Rembe, and J. Gertheiss. “Towards a remote EEG for use in robotic sensors”. In: Forum Bildverarbeitung 2020. KIT Scientific Publishing. 2020, pp. 225–237.
   
• F. Merz et al. “Mechanisms for Scheduling Jobs with Unknown Processing Timeson Unrelated Machines”. In: Preproceedings of 2nd International Workshop on Simulation Science (SimScience 2019). Clausthal-Zellerfeld, Germany, 2019.
   
• F. Merz et al. “Mechanisms for Scheduling Jobs with Unknown Processing Timeson Unrelated Machines”. In: Book of abstracts of 30th European Conference on Operational Research. Dublin, Ireland, 2019, p. 291.