Robotics in agrifood systems

This project aims to develop expertise for robot systems that use self-learning and other machine learning approaches so these systems can operate in dynamic, unstructured environments. This involves situations with many interactions between robots, humans, plants and animals. The focus of the project is on technology and the ethical and social aspects of robot innovation in real-world environments.

Societal and social values play an important role in the development of autonomous robots. It is also important to take into account the requirements imposed by the dynamic environment of plants, obstacles and animals. By including this in the design process, this becomes a value sensitive design. Society and technology must change to make innovation a success. This co-evolution also involves the preferences of the end users of the machine learning robots. Technical innovation is linked to social innovation, whereby we can contribute to social and business goals. From a technology perspective, on the one hand we will explore role of robots to solve current agrifood challenges, on the other hand, current robotic systems are not always designed to consider the key role of robot-nature-human (RNH) interactions. Therefore, it becomes necessary to extend current robots to incorporate these inherent RNH interactions they are part of by exploring through supervised as well as self-learning paradigms based on data-driven and data-hungry learning algorithms. Think of deep reinforcement learning and also methods that require a lot of sensor data to perform robot actions. For autonomous navigation, algorithms are used that make use of services that exchange data.