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Erik Derner 

Ph.D. Student & Junior Researcher
Czech Technical University in Prague
Czech Republic

About me

I'm a Ph.D. student of Control Engineering and Robotics at the Czech Technical University in Prague, supervised by Prof. Robert Babuška. My main fields of interest comprise 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 a 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. Currently, I'm pursuing a Ph.D. with the main research focus on long-term autonomy of mobile robots.

International experience

I find it very inspiring to work in an international environment. I have taken the opportunity to go for several one- or two-semestral study or research stays during my studies:

  • Technical University of Denmark (DTU), Lyngby (Copenhagen), Denmark (2013–2014)
  • University of Ljubljana, Slovenia — ViCoS Lab, Faculty of Computer and Information Science (2015)
  • TU Delft, The Netherlands — Cognitive Robotics, Faculty of 3mE (2017)
  • Carlos III University of Madrid (UC3M), Leganés (Madrid), Spain — Robotics Lab, Escuela Politécnica Superior (2018–2019)

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 developing methods for long-term robot autonomy.

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

Publications

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.
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Derner, E., Kubalík, J., & Babuška, R. (2021). Selecting Informative Data Samples for Model Learning Through Symbolic Regression. IEEE Access (9), January 2021, 14148–14158.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Kubalík, J., Žegklitz, J., Derner, E., & Babuška, R. (2019). Symbolic Regression Methods for Reinforcement Learning. arXiv preprint arXiv:1903.09688.
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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.
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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.
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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.
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Teaching

Courses

2017, fall semester — 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:

  • Using Model Learning Actor-Critic (MLAC) for Reinforcement Learning with Symbolic Regression — Loi Do, summer internship (2017)
  • Life-long Visual Localization of a Mobile Robot in Changing Environments — Kristýna Kumpánová, M.Sc. thesis (2018)
  • Visual Navigation Using Deep Reinforcement Learning — Jonáš Kulhánek, B.Sc. thesis (2019)