Daniel Swoboda

I'm a doctoral researcher in Computer Science at RWTH Aachen University, advised by Professor Hector Geffner in the Chair of Machine Learning & Reasoning (i6).

My work sits between machine learning and symbolic planning. Lately that means teaching robots to plan and act in cluttered, real-world scenes: learning to guide planning with signals about which actions are physically feasible, and learning to turn abstract symbolic plans into the concrete goals a low-level controller can execute. The throughline is bridging the pattern-recognition strength of deep learning with the transparency and structure of symbolic reasoning.

The long-term goal: AI that is more reliable, transparent, and capable of human-like reasoning — particularly for robots acting in complex, dynamic, real-world environments.