Header Image

Industrial AI

autonomous systems and robotics; AI agents and multi-agent systems; cyber-physical systems; Industry 4.0 and smart manufacturing; mobility and autonomous driving; digital twins and simulation

6
1
9

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

Samuel Brucker
Samuel Brucker Depth estimation and sensor fusion for driving
Lili Gao
Lili Gao Diffusion models and data synthesis for driving
Filippo Ghilotti
Filippo Ghilotti Pseudo-labeling and 3D perception for driving
Nicolas Leins
Nicolas Leins Human⁠–⁠AI interaction and augmented reality
Edoardo Palladin
Edoardo Palladin Perception and planning for autonomous driving

Talks and posters

Conference and workshop talks

Aug 2026
FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students by Nicolas Leins
IJCAI Conference, Bremen

Poster presentations

Aug 2026
FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students by Nicolas Leins
IJCAI Conference, Bremen
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 and reports

  1. 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_GhilottiEtAl_Truckdrivedataset_2603-02413,
      archiveprefix = {arXiv},
      eprint = {2603.02413},
      arxiv = {arXiv:2603.02413},
      primaryclass = {cs.CV},
      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}
    }
  2. 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_GhilottiEtAl_Unilipspseudolabeling_2601-05105,
      archiveprefix = {arXiv},
      eprint = {2601.05105},
      arxiv = {arXiv:2601.05105},
      primaryclass = {cs.CV},
      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}
    }
  3. Gonnermann-Müller, J., Haase, J., Leins, N., Kosch, T., and Pokutta, S. (2026). LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles. [arXiv]
    [BibTeX]
    @misc{2026_GonnermannmuellerEtAl_Llmstudentsimulation_2605-06307,
      archiveprefix = {arXiv},
      eprint = {2605.06307},
      arxiv = {arXiv:2605.06307},
      primaryclass = {cs.HC},
      author = {Gonnermann-Müller, Jana and Haase, Jennifer and Leins, Nicolas and Kosch, Thomas and Pokutta, Sebastian},
      title = {LLM-Based Educational Simulation: Evaluating Temporal Student Persona Stability Across ADHD Profiles},
      date = {2026-05-07}
    }
  4. Haase, J., Gonnermann-Müller, J., Yim, S. H., Leins, N., Mendling, J., and Pokutta, S. (2026). Simulating Eating Disorder Patients with LLMs: Evaluating Psychological Persona Stability in Multi-Turn Conversations. [arXiv]
    [BibTeX]
    @misc{2026_HaaseEtAl_Llmstability_2606-26109,
      archiveprefix = {arXiv},
      eprint = {2606.26109},
      arxiv = {arXiv:2606.26109},
      primaryclass = {cs.CY},
      author = {Haase, Jennifer and Gonnermann-Müller, Jana and Yim, See Heng and Leins, Nicolas and Mendling, Jan and Pokutta, Sebastian},
      title = {Simulating Eating Disorder Patients with LLMs: Evaluating Psychological Persona Stability in Multi-Turn Conversations},
      date = {2026-05-12}
    }
  5. Leins, N., Haase, J., Geronimus, V., Gonnermann-Müller, J., and Pokutta, S. (2026). Prompting Against Persona Drift: Comparing Intervention Timing and Content in LLM-Simulated Conversations. [arXiv]
    [BibTeX]
    @misc{2026_LeinsEtAl_PersonaDriftIntervention_2609-24532,
      archiveprefix = {arXiv},
      eprint = {2609.24532},
      arxiv = {arXiv:2609.24532},
      primaryclass = {cs.HC},
      author = {Leins, Nicolas and Haase, Jennifer and Geronimus, Varvara and Gonnermann-Müller, Jana and Pokutta, Sebastian},
      title = {Prompting Against Persona Drift: Comparing Intervention Timing and Content in LLM-Simulated Conversations},
      date = {2026-09-21}
    }
  6. Leins, N., Pelleriti, N., Gonnermann-Müller, J., and Pokutta, S. (2026). When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Cost, and Task Difficulty. [arXiv]
    [BibTeX]
    @misc{2026_LeinsPelleritiGonnermannmuellerPokutta_Llmorchestration_2608-00685,
      archiveprefix = {arXiv},
      eprint = {2608.00685},
      arxiv = {arXiv:2608.00685},
      primaryclass = {cs.AI},
      author = {Leins, Nicolas and Pelleriti, Nico and Gonnermann-Müller, Jana and Pokutta, Sebastian},
      title = {When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Cost, and Task Difficulty},
      date = {2026-08-01}
    }

Conference contributions

  1. 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. Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction, 989–993. Associated with LBR @ HRI 2026. DOI: 10.1145/3776734.3794542 [URL] [arXiv]
    [BibTeX]
    @inproceedings{2026_LeinsGonnermannmuellerTeichmannPokutta_Multiagentar_2603-00016,
      year = {2026},
      date = {2026-03-04},
      booktitle = {Companion Proceedings of the 21st ACM/IEEE International Conference on Human-Robot Interaction},
      publisher = {Association for Computing Machinery},
      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}
    }
  2. Gonnermann-Müller, J., Haase, J., Leins, N., Igel, M., Fackeldey, K., and Pokutta, S. (2026). FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students. Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. Associated with IJCAI 2026. [arXiv]
    [BibTeX]
    @inproceedings{2026_GonnermannmuellerEtAl_Facetmultiagent_2601-22788,
      year = {2026},
      booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence},
      publisher = {International Joint Conferences on Artificial Intelligence Organization},
      archiveprefix = {arXiv},
      eprint = {2601.22788},
      arxiv = {arXiv:2601.22788},
      primaryclass = {cs.HC},
      author = {Gonnermann-Müller, Jana and Haase, Jennifer and Leins, Nicolas and Igel, Moritz and Fackeldey, Konstantin and Pokutta, Sebastian},
      title = {FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students}
    }
  3. Leins, N., Pelleriti, N., Gonnermann-Müller, J., and Pokutta, S. (2026). When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Resources, and Task Difficulty. Accepted at NeurIPS 2026 Workshop on Resource-Aware Agentic AI. [arXiv]
    [BibTeX]
    @misc{2026_LeinsPelleritiGonnermannmuellerPokutta_Llmorchestration_2608-00685:1,
      howpublished = {Accepted at NeurIPS 2026 Workshop on Resource-Aware Agentic AI},
      year = {2026},
      archiveprefix = {arXiv},
      eprint = {2608.00685},
      arxiv = {arXiv:2608.00685},
      primaryclass = {cs.AI},
      author = {Leins, Nicolas and Pelleriti, Nico and Gonnermann-Müller, Jana and Pokutta, Sebastian},
      title = {When Does LLM Orchestration Pay Off? A Controlled Evaluation of Accuracy, Resources, and Task Difficulty}
    }

Projects

Ongoing Projects

GENAD
Generative AI for Scalable Autonomous Driving Systems

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.

GENAD
Mar 2026 to Mar 2029
5