The research in WASP can be illustrated as a matrix with two dimensions, a strategic dimension and a thematic dimension. The strategic dimension emphasizes areas of impact on individuals, society, and industry, whereas the aim of the thematic areas is to encapsulate the underlying scientific and technological challenges that are common to all types of autonomous systems.
The strategic areas of WASP are (i) AI; (ii) Autonomous Systems; and (iii) Software. The thematic areas are (a) Perception and Sensing; (b) Control and Decision Making; (c) Machine Learning and Knowledge Representation; (d) Interaction and Collaboration; (e) Software Technologies and Methods; and (f) Mathematical Foundations and Theory.
Organizationally, WASP is structured in two parts:
where the WASP-AI part also consists of two parts:
- Machine Learning (ML), Deep Learning (DL) and next generation/explainable AI (WASP-AI/MLX),
- Mathematical Foundations of AI (WASP-AI/MATH).