Dr Anastassia Küstenmacher

PostDoc at H-BRSHead of AI group at measX GmbH & Co

Field of research

  • Machine learning and deep learning
  • Fault diagnosis and recovery
  • Analysis of time-series data
  • Anomaly detection and external faults in robotics


Anastassia Küstenmacher
anastassia.kuestenmacher [at] h-brs.de

Sankt Augustin

Grantham-Allee 20
Sankt Augustin
C 203

I currently have two positions: PostDoc at H-BRS and Head of AI group at measX GmbH & Co KG (a company specialised on data analysis). I received my PhD degree at RWTH Aachen in 2018. My PhD Thesis deals with the topics of making robots able to learn from their faults. The work was supervised by Professor Gerhard Lakemeyer (RWTH Aachen), Professor Paul Plöger (H-BRS) and Professor Gerald Steinbauer (TU Graz). I obtained my Diploma in Applied Mathematics in 2000 in Irkutsk State University, Russia and my master's degree in Autonomous Systems in 2009 from H-BRS.



  • Lecture (selbstständige Lehre) WS 2020/21: Machine Learning (Master)
  • Co-Lecture (selbstständige Lehre) WS 2020/21: R&D Colloquium, together with M.Sc. Iman Awaad (Master)
  • Lecture (selbstständige Lehre) WS 2020/21: Literature Seminar (Bachelor)
  • Lecture (selbstständige Lehre) SS 2020: Machine Learning (Master)
  • Co-Lecture (selbstständige Lehre) SS 2020: R&D Colloquium, together with M.Sc. Iman Awaad  (Master)
  • Lecture (selbstständige Lehre) WS 2019/20: Machine Learning (Master)
  • Co-Lecture (selbstständige Lehre)WS 2019/20: Neural Network, together with Prof. Dr. Paul G. Plöger (Master)
  • Lecture (selbstständige Lehre) SS 2019: Leaning and Adaptivity (Master)
  • Co-Lecture (selbstständige Lehre) SS 2019: R&D Colloquium, together with M.Sc. Iman Awaad  (Master)
  • TA WS 2018/19: Neural Network (Master)
  • Co-Lecture (selbstständige Lehre) SS 2018: Learning and Adaptivity, together with Dr. Matias Valdenegro-Toro, DFKI (Master)
  • Lecture (selbstständige Lehre) SS 2018: Adaptive Filtering (Master)
  • TA WS 2017/18: Neural Network (Master)
  • TA SS 2016: Probabilistic Reasoning (Master)
  • TA WS 2013/14: Neural Network (Master)
  • TA SS 2013: Adaptive Filtering (Master)
  • TA SS 2013: Probabilistic Reasoning (Master)
  • TA WS 2012/13: Neural Network (Master)
  • TA SS 2012: Adaptive Filtering (Master)
  • TA SS 2012: Probabilistic Reasoning (Master)
  • TA WS 2011/12: Neural Network (Master)
  • TA WS 2011/12: Robot Manipulation (Master)
  • TA SS 2011: Control & Systems Theory (Master)
  • TA SS 2011: Probabilistic Reasoning (Master)
  • TA WS 2010/11: Control & Systems Theory (Master)
  • TA WS 2010/11: Hardware and Software Co-Design (Master)
  • TA SS 2010: Control & Systems Theory (Master)
  • TA WS 2009/10: Control & System Theory (Master)
  • TA SS 2009: Control & Systems Theory (Master)


  • DAAD PPP-Project on Defending robotic vision against adversarial attacks; H-BRS and University of West Australia in Perth, under review 
  • Karrierewege FH-Professur”, NRW-Landesprogramm, Project manager, 2019-2022
  • Startföderung funded by H-BRS, Project coordinator, 2020-2021
  • Verbundprojekt AKoS: Akustische Kontrolle von Schweißnähten bei sicherheitskritischen Bauteilen im Rahmen der Qualitätssicherung”, Project member 2020-2021
  • Accelerating the Innovation Cycle in Service Robotics” (AICISS) project funded by BMWI, Project coordinator, 2014-2015
  • Deutsch-Russische Robotik Initiative” (DRRI) project funded by BMBF, Project coordinator, 2011-2012
  • DESIRE, funded by BMBF, Research associate, 2005-2009



A. Küstenmacher and P. G. Plöger, Symbolic Representation of Execution Specific Knowledge 2019 In Proceedings of 30th International Workshop on Principles of Diagnosis DX’19


A. Mitrevski, A. Kuestenmacher, S. Thoduka, and P. G. Plöger. Improving the reliability of service robots in the presence of external faults by learning action execution models, In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, Singapore


A. Kuestenmacher and P. G. Plger, Model-Based Fault Diagnosis Techniques for Mobile Robots. 9th IFAC Symposium on Intelligent Autonomous Vehicles (IAV 2016), 2016, Leipzig, Germany.

A. Drak, Y. Youssef, P. G. Plöger, A. Kuestenmacher Remote Fault Diagnosis of Robots Using a Robotic Black Box. In Proceedings of 27th International Workshop on Principles of Diagnosis (DX’16), 2016, Denver, Colorado, USA


A. Küstenmacher P. Plöger and G. Lakemeyer, Enhancing Action Execution by Using Spatial Relational Knowledge. In Proceedings of 25nd International Workshop on Principles of Diagnosis (DX’14), 2014, Graz, Austria.

A. Kuestenmacher, N. Akhtar, P. Plöger and G. Lakemeyer. Towards Robust Task Executio for Domestic Service Robots. In the 24th International Conference on Automated Planning and Scheduling (ICAPS), 2014, Portsmouth, USA.


A. Küstenmacher, N. Akhtar, P. Plöger and G. Lakemeyer Unexpected Situations in Service Robot Environment: Classification and Reasoning Using Naive Physics. 2013. In the 17th annual RoboCup International Symposium 2013

A. Küstenmacher, P. Plöger and G. Lakemeyer Improving Robustness of Task Execution Against External Faults Using Simulation Based Approach, 2013 In Proceedings of 24nd International Workshop on Principles of Diagnosis DX’13.

A. Küstenmacher, N. Akhtar, P. Plöger and G. Lakemeyer Towards Robust Task Execution for Domestic Service Robots. 2013. In Journal of Intelligent & Robotic Systems, Special Issues on Advances in Robotics

N. Akhtar, A. Küstenmacher, P. Plöger and G. Lakemeyer Simulation-based approach for avoiding external faults. 2013, The 16th International Conference on Advanced Robotics, ICAR 2013


A. Küstenmacher, P. Plöger Model-Based Diagnosis of Faults in Robotics 2012. In Proceedings of 23nd InternationalWorkshop on Principles of Diagnosis DX’12.

A. Küstenmacher, P. Plöger Categorization of External Unknown Faults in Robotics 2012. In Proceedings of 23nd InternationalWorkshop on Principles of Diagnosis DX’12.

A. Küstenmacher. Methods for failure detection for mobile manipulation 2012. Technical Report University of Applied Science Bonn-Rhein-Sieg, Germany


N. Akhtar, A. Küstenmacher Using Naive Physics for unknown external faults in robotics. 2011. In Proceedings of 22nd International Workshop on Principles of Diagnosis DX’11

More infos



  • Winterschool “Advanced Machine Learnig”, 2020, St. Augustin, Germany
  • IAV 2016 - 9th IFAC Symposium on Intelligent Autonomous Vehicles in Leipzig, Germany
  • ROBOLYMPICS 2012 - Competition for German and Russian students. In the competition participated six teams from Russian and German universities, Moscow, Russia
  • First “German Russian Conference on Robotics” (GRCR) 2012, Munich, Germany

Invited Talks:

  • Simulation-based techniques for prediction of unknown external faults, International Scientific-and-Technological Conference, EXTREME ROBOTICS, Saint Petersburg, Russia, 2011.
  • Fault reasoning based on Naive Physics, ICRA Workshop on Automated Diagnosis, Repair and Re-Configuration of Robot Systems, (ICRA 2011), Shanghai, China Refereed Conference/Workshop 


  • International Workshop on Principles of Diagnosis (DX)
  • IFAC Symposium on Intelligent Autonomous Vehicles (IAV)
  • Conference on Artificial Intelligence (AAAI)
  • Intelligent Robots and Systems (IROS)
  • IJCAI Conference
  • Journal "Advanced Robotics"
  • Journal of Intelligent & Robotic Systems