Publications

Giuri, M.*, Jackermeier, M.*, Abate, A. (2025). Zero-Shot Instruction Following in RL via Structured LTL Representations. In ICML’25 Workshop on Programmatic Representations for Agent Learning. (arXiv)

Jackermeier, M., Abate, A. (2025). DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL. In ICLR’25 (oral). (arXiv) (website)

Jackermeier, M., Chen, J., and Horrocks, I. (2024). Dual Box Embeddings for the Description Logic EL++. In WWW’24 (oral). (arXiv)

Ashok, P., Jackermeier, M., Křetínský, J., Weinhuber, C., Weininger, M., Yadav, M. (2021). dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Experts. In TACAS’21. (arXiv)

Ashok, P.*, Jackermeier, M.*, Jagtap, P., Křetínský, J., Weininger, M.*, Zamani, M. (2020). dtControl: Decision Tree Learning Algorithms for Controller Representation. In HSCC’20. (arXiv)

Preprints

Jackermeier, M., Giuri, M., Cloete, J., Abate, A. (2026). Zero-Shot Instruction Following in RL via Structured LTL Representations. Extension of our previous workshop paper. (arXiv)

Schnitzer, Y.*, Jackermeier, M.*, Abate, A., Parker, D. (2026). Probabilistic Performance Guarantees for Multi-Task Reinforcement Learning. (arXiv)

Cloete, J., Jackermeier, M., Havoutis, I., Abate, A. (2026). PlatoLTL: Learning to Generalize Across Symbols in LTL Instructions for Multi-Task RL. (arXiv)

Abate, A., De Giacomo, G., Jackermeier, M.*, Křetínský, J., Prokop, M.*, Weinhuber, C.* (2026). Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions. (arXiv)

Theses

Description Logic Embeddings for Neuro-Symbolic Reasoning, MSc Dissertation, University of Oxford. 2022.

dtControl: Decision Tree Learning for Explainable Controller Representation, Bachelor’s Thesis, Technical University of Munich. 2020.