. .

Erik Derner 

Postdoctoral Researcher
Czech Technical University in Prague
Czech Republic

About me

I hold a Ph.D. degree from the Czech Technical University in Prague. I did my Ph.D. in the field of Control Engineering and Robotics, supervised by Prof. Robert Babuška. My main fields of interest comprise human-centric artificial intelligence, robotics, computer vision, reinforcement learning, and genetic algorithms. I'm a member of the project Robotics for Industry 4.0 (R4I) at the Czech Institute for Informatics, Robotics, and Cybernetics (CIIRC).

Education

I have completed the B.Sc. and M.Sc. study program Open Informatics at the Faculty of Electrical Engineering, Czech Technical University in Prague, which gave me a solid background in artificial intelligence and mathematics.

International experience

I find it very inspiring to work in an international environment. I have taken the opportunity to go for several study or research stays:

  • ELLIS Alicante, Alicante, Spain — Institute of Human-Centered AI (2022, 2 months)
  • Carlos III University of Madrid (UC3M), Leganés (Madrid), Spain — Robotics Lab, Escuela Politécnica Superior (2018–2019 & 2021, 10 months in total)
  • TU Delft, Delft, The Netherlands — Cognitive Robotics, Faculty of 3mE (2017, 3 months)
  • University of Ljubljana, Ljubljana, Slovenia — ViCoS Lab, Faculty of Computer and Information Science (2015, 1 semester)
  • Technical University of Denmark (DTU), Lyngby (Copenhagen), Denmark (2013–2014, 1 semester)

Research

Projects

Currently, I am involved in the project Robotics for Industry 4.0, led by Prof. Robert Babuška. I am working mostly on tasks from WP1 focused on data-efficient model learning methods and long-term autonomy of mobile robots.

In addition, I am working on the safety aspects of conversational AI algorithms in collaboration with ELLIS Alicante, led by Dr. Nuria Oliver.

I have also contributed to the project Symbolic Regression for Reinforcement Learning (SR4RL).

Publications

Derner, E., & Batistič, K. (2023). Beyond the Safeguards: Exploring the Security Risks of ChatGPT. arXiv preprint arXiv:2305.08005.
 PDF    BIB

Kubalík, J., Derner, E., & Babuška, R. (2023). Neural Networks for Symbolic Regression. arXiv preprint arXiv:2302.00773.
 PDF    BIB

Derner, E., & Zahálka, J. (2022). Benefits and Risks of AI Companions. Poster presented at the ELLIS Doctoral Symposium 2022, Alicante, Spain, September 2022.
 PDF    BIB

Kulhánek, J., Derner, E., Sattler, T., & Babuška, R. (2022). ViewFormer: NeRF-Free Neural Rendering from Few Images Using Transformers. In 17th European Conference on Computer Vision, ECCV 2022, 198–216, Tel Aviv, Israel.
 PDF    BIB    WEB    GIT

Derner, E. (2022). Data-Efficient Methods for Model Learning and Control in Robotics. Doctoral Thesis. Czech Technical University in Prague. Defended in May 2022.
 PDF    BIB

Vastl, M., Kulhánek, J., Kubalík, J., Derner, E., & Babuška, R. (2022). SymFormer: End-to-End Symbolic Regression Using Transformer-Based Architecture. arXiv preprint arXiv:2205.15764.
 PDF    BIB    WEB    GIT

Derner, E., Kubalík, J., & Babuška, R. (2021). Guiding Robot Model Construction with Prior Features. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 7112–7118, Prague, Czech Republic.
 PDF    BIB

Kubalík, J., Derner, E., & Babuška, R. (2021). Multi-Objective Symbolic Regression for Physics-Aware Dynamic Modeling. Expert Systems with Applications (182), November 2021, 115210.
 PDF    BIB

Kubalík, J., Derner, E., Žegklitz, J., & Babuška, R. (2021). Symbolic Regression Methods for Reinforcement Learning. IEEE Access (9), October 2021, 139697–139711.
 PDF    BIB

Kulhánek, J., Derner, E., & Babuška, R. (2021). Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning. IEEE Robotics and Automation Letters 6(3), 4345-4352.
 PDF    BIB    WEB    GIT

Derner, E., Kubalík, J., & Babuška, R. (2021). Selecting Informative Data Samples for Model Learning Through Symbolic Regression. IEEE Access (9), January 2021, 14148–14158.
 PDF    BIB    VIDEO

Derner, E., Gómez, C., Hernández, A. C., Barber, R., & Babuška, R. (2021). Change Detection Using Weighted Features for Image-Based Localization. Robotics and Autonomous Systems (135), January 2021, 103676.
 PDF    BIB

Hernández, A. C., Derner, E., Gómez, C., Barber, R., Babuška, R. (2020). Efficient Object Search Through Probability-Based Viewpoint Selection. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 6172–6179, Las Vegas, NV, USA.
 PDF    BIB

Gómez, C., Hernández, A. C., Derner, E., Barber, R., & Babuška, R. (2020). Object-Based Pose Graph for Dynamic Indoor Environments. IEEE Robotics and Automation Letters 5(4), 5401–5408.
 PDF    BIB

Derner, E., Kubalík, J., Ancona, N., & Babuška, R. (2020). Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression. Applied Soft Computing (94), September 2020, 106432.
 PDF    BIB

Kubalík, J., Derner, E., & Babuška, R. (2020). Symbolic Regression Driven by Training Data and Prior Knowledge. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '20), 958–966, Association for Computing Machinery, New York, NY, USA.
 PDF    BIB

Derner, E., Gómez, C., Hernández, A. C., Barber, R., & Babuška, R. (2019). Towards Life-Long Autonomy of Mobile Robots Through Feature-Based Change Detection. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
 PDF    BIB

Kulhánek, J., Derner, E., de Bruin, T., & Babuška, R. (2019). Vision-based Navigation Using Deep Reinforcement Learning. In 2019 European Conference on Mobile Robots (ECMR), 1–8, Prague, Czech Republic.
 PDF    BIB    WEB    GIT

Gómez, C., Hernández, A. C., Derner, E., & Barber, R. (2019). Semantic Localization Through Propagation of Scene Information in a Hierarchical Model. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
 PDF    BIB

Hernández, A. C., Gómez, C., Derner, E., Barber, R. (2019). Indoor Scene Recognition Based on Weighted Voting Schemes. In 2019 European Conference on Mobile Robots (ECMR), 1–6, Prague, Czech Republic.
 PDF    BIB

Derner, E., Kubalík, J., & Babuška, R. (2018). Reinforcement Learning with Symbolic Input-Output Models. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3004–3009, Madrid, Spain.
 PDF    BIB    VIDEO

Derner, E., Kubalík, J., & Babuška, R. (2018). Data-driven Construction of Symbolic Process Models for Reinforcement Learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), 5105–5112, Brisbane, Australia.
 PDF    BIB    VIDEO

Kubalík, J., Derner, E., & Babuška, R. (2017). Enhanced Symbolic Regression Through Local Variable Transformations. In 2017 International Joint Conference on Computational Intelligence (IJCCI), 91–100, Funchal, Madeira, Portugal.
 PDF    BIB

Teaching

Courses

2017, 2021–2022 — lab sessions of the course Dynamics and Control of Networks at the Faculty of Electrical Engineering, Czech Technical University in Prague.

Student works

Feel most welcome to contact me if you are interested in working on topics related to localization and navigation of mobile robots, object detection and classification, reinforcement learning, etc. We can identify together a suitable task for your student project, thesis etc.

Finished works and works in progress:

  • Co-Evolutionary Approach to Symbolic Regression — Přemysl Pilař, B.Sc. thesis (2023)
  • Real-Time Assistance for Visually Impaired Individuals — Ernesto Iván Ochoa Hidalgo, M.Sc. thesis (2023)
  • Visual Navigation Using Deep Reinforcement Learning — Jonáš Kulhánek, B.Sc. thesis (2019)
  • Life-long Visual Localization of a Mobile Robot in Changing Environments — Kristýna Kumpánová, M.Sc. thesis (2018)
  • Using Model Learning Actor-Critic (MLAC) for Reinforcement Learning with Symbolic Regression — Loi Do, summer internship (2017)