What we are interested in
At iol.indai we research and develop autonomous systems and intelligent agents for industrial and cyber-physical applications. Our work sits at the intersection of AI, robotics, and Industry 4.0, where we design AI agents and multi-agent systems that perceive, reason, and act in complex real-world environments; autonomous robotics, developing intelligent systems for manufacturing, logistics, and human-robot collaboration; cyber-physical systems, integrating sensing, computation, and actuation for smart factories and connected infrastructure; and mobility applications, including autonomous driving, fleet coordination, and intelligent transportation systems. We leverage digital twins and simulation environments to develop, test, and deploy AI systems that bridge the gap between research and industrial practice, enabling the next generation of autonomous and adaptive industrial systems.
Researchers
Doctoral candidates
brucker (at) zib.de
gao (at) zib.de
ghilotti (at) zib.de
leins (at) zib.de
palladin (at) zib.de
💬 Talks and posters
Poster presentations
- Mar 2026
- Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training by Nicolas Leins
21st HRI Conference, Edinburgh
📝 Publications and preprints
Preprints
- Ghilotti, F., Palladin, E., Brucker, S., Sigal, A., Bijelic, M., and Heide, F. (2026). TruckDrive: Long-Range Autonomous Highway Driving Dataset.
[arXiv]
[BibTeX]
@misc{2026_FilippoghilottiEtAl_Truckdrivedataset_2603-02413, archiveprefix = {arXiv}, eprint = {2603.02413}, arxiv = {arXiv:2603.02413}, primaryclass = {cs.CV}, year = {2026}, author = {Ghilotti, Filippo and Palladin, Edoardo and Brucker, Samuel and Sigal, Adam and Bijelic, Mario and Heide, Felix}, title = {TruckDrive: Long-Range Autonomous Highway Driving Dataset}, date = {2026-03-02} } - Ghilotti, F., Brucker, S., Saidy, N., Matteucci, M., Bijelic, M., and Heide, F. (2026). UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition.
[arXiv]
[BibTeX]
@misc{2026_FilippoghilottiEtAl_Unilipspseudolabeling_2601-05105, archiveprefix = {arXiv}, eprint = {2601.05105}, arxiv = {arXiv:2601.05105}, primaryclass = {cs.CV}, year = {2026}, author = {Ghilotti, Filippo and Brucker, Samuel and Saidy, Nahku and Matteucci, Matteo and Bijelic, Mario and Heide, Felix}, title = {UniLiPs: Unified LiDAR Pseudo-Labeling with Geometry-Grounded Dynamic Scene Decomposition}, date = {2026-01-08} }
Conference proceedings
- Leins, N., Gonnermann-Müller, J., Teichmann, M., and Pokutta, S. (2026). Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training. Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), 989–993.
DOI: 10.1145/3776734.3794542
[URL]
[arXiv]
[BibTeX]
@inproceedings{2026_NicolasleinsJanagonnermannmuellerMalteSebastianpokutta_Multiagentar_2603-00016, year = {2026}, date = {2026-03-04}, booktitle = {Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI)}, month = mar, pages = {989--993}, doi = {10.1145/3776734.3794542}, url = {https://dl.acm.org/doi/10.1145/3776734.3794542}, archiveprefix = {arXiv}, eprint = {2603.00016}, arxiv = {arXiv:2603.00016}, primaryclass = {cs.RO}, author = {Leins, Nicolas and Gonnermann-Müller, Jana and Teichmann, Malte and Pokutta, Sebastian}, title = {Beyond Static Instruction: A Multi-agent AI Framework for Adaptive Augmented Reality Robot Training} }
🔬 Projects
Ongoing Projects
The project aims at shaping the next generation of highway autonomy for heavy trucks by combining long-range multimodal data, controllable generative models, a closed-loop virtual gym, and vision-language model (VLM) embedded reasoning. Rather than treating autonomy as a pure perception or planning problem, the project aims to advance generative simulation, reinforcement learning and chain-of-thought reasoning and language-guided representation learning as a unified research agenda.
