Springe zum Hauptinhalt

Lab Publications


Kaden, S., Schwarz, L., Röhrbein, F. (2024): A Research Platform for Human-Robot-Interaction with Focus on Collaborative Assembly Scenarios. IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Aug 26 - Aug 30, 2024 to be held in Pasadena, California.
Röhrbein, F., Kress K.: The Digital Laboratory, Artificial Intelligence and the Interface Standard LADS. Trend Report 2024 - Analytical, Bio and Laboratory Technology for Markets, Developments, Potential, p. 14-15.
Qu, S., Zou, T., He, L., Röhrbein, F., Knoll, A., Chen, G., & Jiang, C. (2024).: Lead: Learning decomposition for source-free universal domain adaptation. arXiv:2403.03421.


Abdelaal O.M. Röhrbein F.: Policy Learning with Spiking Neural Network for Robot Manipulation Tasks . 1st International Conference on Hybrid Societies 2023. Monarch.qucosa.de.

Noack B., Röhrbein F., Notni G.: Event-Based Sensor Fusion in Human-Machine Teaming. 60th ILMENAU SCIENTIFIC COLLOQUIUM (2023).

Röhrbein F., Senden M. (2023) Editorial: The embodied brain: computational mechanisms of integrated sensorimotor interactions with a dynamic environment volume II. Front. Comput. Neurosci. 17:1203717. doi: 10.3389/fncom.2023.1203717


Feldotto B., Lengenfelder H., Röhrbein F., Knoll A.C. Network Layer Analysis for a RL-Based Robotic Reaching Task.  Frontiers in Robotics and AI , Volume 9, 2022,  doi:10.3389/frobt.2022.799644

Abbas A.A, Röhrbein F., Hamker F.. The Role of the Superior Colliculus in Attention Demanding Tasks: A Computational Model.  forum.fens.org/abstract-e-book/

Kuske N., Ragni M., Röhrbein F., Vitay J., Hamker F.: Demands and potentials of different levels of neuro-cognitive models für human spatial cognition. In, E. Ferstl, L. Konieczny, & R. Stülpnagel (Eds.), Proceedings of KogWiss2022, the 15th Biannual Conference of the German Society for Cognitive Science (pp. 115-116). Albert-Ludwigs-Universität Freiburg. doi:10.6094/UNIFR/229611

Vijayan P., Atoofi P., Fietzek T., Röhrbein F.: Trajectory learning for an iCub arm generated by CPGs. Bernstein Conference 2022.  doi:10.12751/nncn.bc2022.194


Röhrbein F.: Frontiers in Neurorobotics–Editor’s Pick 2021. pdf here.


Aljalbout, E., Walter, F., Röhrbein, F., Knoll, A.: Task-independent spiking central pattern generator: A learning-based approach. Neural Processing Letters 51 (3), 2751-2764. doi.org/10.1007/s11063-020-10224-9

Chen, G., Cao, H., Conradt, J., Tang, H., Röhrbein, F., Knoll, A.: Event-based neuromorphic vision for autonomous driving: a paradigm shift for bio-inspired visual sensing and perception. IEEE Signal Processing Magazine 37 (4), 34-49. doi:10.1109/MSP.2020.2985815


Chen, G., Cao, H., Ye, C., Zhang, Z., Liu, X., Mo, X., Qu, Z., Conradt, J., Röhrbein, F., Knoll, F. (2019): Multi-cue event information fusion for pedestrian detection with neuromorphic vision sensors. Frontiers in Neurorobotics. Vol. 13, p. 10. doi:10.3389/fnbot.2019.00010


Chen, G., Cao, H., Aafaque, M, Chen, J., Ye, C., Röhrbein, F., Conradt, J., Chen, K., Bing, Z., Liu, X., Hinz, G., Stechele, W., Knoll, A. (2018): Neuromorphic vision-based multivehicle detection and tracking for intelligent transportation systems. Journal of Advanced Transportation. doi:10.1155/2018/4815383

Bing, Z., Meschede, C., Röhrbein, F., Huang, K., Knoll, A.: A survey of robotics control based on learning-inspired spiking neural networks. Frontiers in Neurorobotics.doi:10.3389/fnbot.2018.00035/



Chen, G., Bing, Z., Röhrbein, F., Conradt, J., Huang, K., Cheng, L., Jiang, Z., Knoll, A. . Towards brain-inspired learning with the neuromorphic snake-like robot and the neurorobotic platform. In IEEE Transactions on Cognitive and Developmental Systems. doi: 10.1109/TCDS.2017.2712712

Zhenshan, B., Cheng, L., Huang, K., Jiang, Z., Chen, G., Röhrbein, F., Knoll, A.. Towards Autonomous Locomotion: Slithering Gait Design of Snake-Like Robot for Target Observation and Tracking. IEEE / RSJ International Conference on Intelligent Robots and Systems (IROS-2017). doi:10.1109/IROS.2017.8206095

Scardapane, S., Stoffl, L., Röhrbein, F., Uncini, A. . On the Use of Deep Recurrent Neural Networks for Detecting Audio Spoofing Attacks. International Joint Conference on Neural Networks (IJCNN-2017): 3483-3490.doi:10.1109/IJCNN.2017.7966294

Hinz, G., Chen, G., Aafaque, M., Röhrbein, F., Conradt, J., Bing, Z., Qu, Z., Stechele, W., Knoll, A. (2017). Online Multi-Object Tracking-by-Clustering for Intelligent Transportation System with Neuromorphic Vision Sensor. German Conference on Artificial Intelligence KI-2017. doi:10.1007/978-3-319-67190-1_11

Walter, F., Sanden, M., Röhrbein, F., Knoll, A. . Towards a Neuromorphic Implementation of Hierarchical Temporal Memory on SpiNNaker. IEEE International Symposium on Circuits and Systems (ISCAS), Baltimore, MD, USA. doi:10.1109/ISCAS.2017.8050983


Vandesompele, A., Walter, F., Röhrbein, F. Neuro-Evolution of Spiking Neural Networks on SpiNNaker Neuromorphic Hardware. IEEE Symposium Series on Computational Intelligence. doi:10.1109/SSCI.2016.7850250


Röhrbein, F., Goddard, P., Schneider, M., James, G., Guo, K.: How does image noise affect actual and predicted human gaze allocation in assessing image quality? Vision Research, 112:11 - 25, 2015. doi:10.1016/j.visres.2015.03.029

Walter, F., Röhrbein, F. Knoll, A.: Computation by Time. Neural Processing Letters, pages 1-22, 2015. doi:10.1007/s11063-015-9478-6


Ostankov, A., Röhrbein, F., Waltinger, U. LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), Reykjavik, Iceland, 2613-2620. pdf here.

Röhrbein, F., Veiga, G., Natale, C.: Gearing Up and Accelerating - Crossfertilization between Academic and Industrial Robotics Research in Europe: Technology transfer experiments from the ECHORD project. Number 94 in Springer Tracts in Advanced Robotics. Springer International Publishing. doi:10.1007/978-3-319-02934-4


Krichmar, J., F. Röhrbein, F.: Value and Reward Based Learning in Neurobots. volume 7 of Frontiers in Neurorobotics. doi:10.3389/fnbot.2013.00013



Röhrbein, F., Eggert, J., Körner, E.: Child-friendly divorcing: Incremental hierarchy learning in Bayesian Networks. Proceedings of International Joint Conference on Neural Networks (IJCNN), 2711-2716. doi:10.1109/IJCNN.2009.5178995


Röhrbein, F., Eggert, J., Körner, E.: Bayesian columnar networks for grounded cognitive systems. In B.C. Love, K. McRae, V.M. Sloutsky (Eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society (CogSci), 1423-1428. pdf here.


Röhrbein, F., Eggert, J., Körner, E.: Prototypical relations for cortex-inspired semantic representations. Proceedings of the 8th International Conference on Cognitive Modeling (ICCM), Ann Arbor, U.S.A. 307-312. pdf here.




Röhrbein, F., Artmann, S., Hirsch, M.: IKAR/OS - Intelligent knowledge and research operating system. In: Rumpe, B., Hesse, W. (Eds): Modellierung, GI-Edition Lecture Notes in Informatics (LNI), Vol. P-45, Köllen Verlag, Bonn: 327-328. pdf here


Röhrbein, F., Zetzsche, C.: The statistics of natural scenes and Weber’s law. In Würtz, P., Lappe, M. (Eds), 4. Workshop Dynamic Perception, Akademische Verlagsgesellschaft, Berlin: 233-238.


Schill, K., Baier, V., Röhrbein, F., Brauer, W.: A hierarchical network model for the analyses of human spatio-temporal information processing. In Rogowitz, B.E., Pappas, T.N. (Eds), Human Vision and Electronic Imaging, Proceedings SPIE. Vol. 4299: 615-621. doi:10.1117/12.429535


Baier, V., Schill, K., Röhrbein, F., Brauer, W.:. Processing of spatio-temporal structures: A hierarchical neural network model. In Baratoff, G., Neumann, H. (Eds), 3. Workshop Dynamic Perception, Vol. 9 of Proceedings in Artificial Intelligence, Berlin: 223- 226.

Eisenkolb, A., Schill, K., Röhrbein, F., Baier, V., Musto, A., Brauer, W.: Visual processing and representation of spatio-temporal patterns. In Freksa, C., Habel, C., Wender, K. (Eds), Spatial Cognition II - Integrating Abstract Theories, Empirical Studies, Formal Methods and Practical Applications, Lecture Notes in Artificial Intelligence, vol. 1849, Springer, Berlin: 145-156. doi:10.1007/3-540-45460-8_11