In this project, we will combine knowledge and experience in human-robot interaction, AI for multi-robot control, and business innovation to develop novel methods for human-swarm interaction. The methods will be evaluated in concrete case studies directly relevant to the industrial partners and their target markets.

Most research in medical AI never makes it to the clinic. We aim to create more clinically useful AI and increase technology acceptance among clinicians by establishing Human-AI collaboration as a target that can be optimized similarly to predictive performance. In terms of explainable AI, this defines a shift from researching what we can explain to also researching how we explain it well.

Today, the manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. In order to improve performance, robustness and consistency of this inspection, there is a need for automated imaging-based solutions to replace subjective manual inspection.