My ORCID is 0000-0003-2824-7956. You can find an overview of my publications at Google scholar. I use this page to collect all information about my publications, including posters, presentations, video, data, and code.
Fabisch, A., Petzoldt, C., Otto, M., Kirchner, F. (2024).
A Survey of Behavior Learning Applications in Robotics - State of the Art and Perspectives.
DFKI RIC Research Report, RR-24-01.
DFKI arxiv.org
Fabisch, A. (2024).
Learning of Cartesian Motion with Movement Primitives.
Journal of Open Source Software, 9(97).
DOI: 10.21105/joss.06695
JOSS Code
Laux, M., Singh, C., Fabisch, A. (2023).
Grasping 3D Deformable Objects via Reinforcement Learning: A Benchmark and Evaluation.
In D. Seita, M. Lippi, M. C. Welle & F. Zhang (Eds.), 3rd Workshop on Representing and Manipulating Deformable Objects @ ICRA2023.
PDF Video Code
Fabisch, A., Uliano, M., Marschner, D., Laux, M., Brust, J., Controzzi, M. (2022).
A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations.
In K. Mombaur, D. Lee & J. Park (Eds.), IEEE-RAS International Conference on Humanoids Robots (HUMANOIDS).
DOI: 10.1109/Humanoids53995.2022.10000165
IEEE Preprint Slides Code Dataset
Fabisch, A., Kirchner, F. (2021).
Sample-Efficient Policy Search with a Trajectory Autoencoder.
In A. Bewley, I. Gilitschenski, M. Itkina, H. Kasaei, J. Kober, N. Lambert, J. Perez, R. Senanayake, V. Vanhoucke, M. Wulfmeier (Eds.), 4th Robot Learning Workshop: Self-Supervised and Lifelong Learning at Neural Information Processing Systems (NeurIPS).
PDF Slides Code
Fabisch, A., Langosz, M., Kirchner, F. (2020).
BOLeRo: Behavior Optimization and Learning for Robots.
International Journal of Advanced Robotic Systems, 17 (3).
DOI: 10.1177/1729881420913741.
IJARS Code
Gutzeit, L., Fabisch, A., Petzoldt, C., Wiese, H., Kirchner, F. (2019).
Automated Robot Skill Learning from Demonstration for Various Robot Systems.
In C. Benzmüller & H. Stuckenschmidth (Eds.), KI 2019: Advances in Artificial Intelligence (pp. 168-181). Springer.
Springer Preprint heise.de
Fabisch, A. (2019).
Empirical Evaluation of Contextual Policy Search with a Comparison-based Surrogate Model and Active Covariance Matrix Adaptation.
In M. Lopez-Ibanez, A. Auger, T. Stützle (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference Companion, (pp. 251-252). Association for Computing Machinery.
DOI: 10.1145/3319619.3321935.
ACM Poster Preprint (long version)
Fabisch, A. (2019).
A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills.
In K. Berns, D. Görges (Eds.), Advances in Intelligent Systems and Computing (Proceedings of RAAD 2019).
Springer Preprint Slides Code
Gutzeit, L., Fabisch, A., Otto, M., Metzen, J. H., Hansen, J., Kirchner, F., Kirchner, E. A. (2018).
The BesMan Learning Platform for Automated Robot Skill Learning.
Frontiers in Robotics and AI, 5 (43).
DOI: 10.3389/frobt.2018.00043
Frontiers
Metzen, J. H., Fabisch, A., Hansen, J. (2015).
Bayesian Optimization for Contextual Policy Search.
In A. Faust (Ed.), Proceedings of the Second Machine Learning in Planning and Control of Robot Motion Workshop (IROS).
PDF Code
Fabisch, A., Metzen, J. H., Krell, M. M., Kirchner, F. (2015).
Accounting for Task-Difficulty in Active Multi-Task Robot Control Learning.
Künstliche Intelligenz, 29, 369-377.
DOI: 10.1007/s13218-015-0363-2
Springer Preprint
Fabisch, A., Metzen, J. H.. (2014).
Active Contextual Policy Search.
Journal of Machine Learning Research, 15, 3371-3399.
JMLR
Metzen, J. H., Fabisch, A., Senger, L., de Gea Fernández, J., Kirchner, E. A. (2014).
Towards Learning of Generic Skills for Robotic Manipulation.
Künstliche Intelligenz, 28, 15-20.
DOI: 10.1007/s13218-013-0280-1
Springer Preprint
Fabisch, A. (2021).
gmr: Gaussian Mixture Regression.
Journal of Open Source Software, 6 (62), 3054.
DOI: 10.21105/joss.03054
JOSS Code
Dominguez, R., Post, M., Fabisch, A., Michalec, R., Bissonnette, V., Govindaraj, S. (2020).
Common Data Fusion Framework: An open-source Common Data Fusion Framework for space robotics.
International Journal of Advanced Robotic Systems, 17 (2).
DOI: 10.1177/1729881420911767
IJARS Code
Fabisch, A. (2019).
pytransform3d: 3D Transformations for Python.
Journal of Open Source Software, 4 (33), 1159.
DOI: 10.21105/joss.01159
JOSS Code
Post, M., Michalec, R., Bianco, A., Yan, X., De Maio, A., Labourey, Q., Lacroix, S., Gancet, J., Govindaraj, S., Martinez-Gonzalez, X., Dalati, I., Domínguez, R., Wehbe, B., Fabisch, A., Rohrig, E., Souvannavong, F., Bissonnette, F., Smisek, M., Oumer, N. W., Meyer, L., Marton, Z.-C.. (2018).
InFuse data fusion methodology for space robotics, awareness and machine learning.
In 69th International Astronautical Congress (IAC). Curran Associates, Inc.
Preprint
Dominguez, R., Govindaraj, S., Gancet, J., Post, M., Michalec, R., Oumer, N., Wehbe, B., Bianco, A., Fabisch, A., Lacroix, S., de Maio, A., Labourey, Q., Souvannavong, F., Bissonnette, V., Smisek, M., Yan, X. (2018).
A Common Data Fusion Framework for Space Robotics: Architecture and Data Fusion Methods.
In International Symposium on Artificial Intelligence, Robotics and Automation in Space (2018)
Preprint
Govindaraj, S., Gancet, J., Post, M., Dominguez, R., Souvannavong, F., Lacroix, S., Smisek, M., Hildalgo-Carrio, J., Wehbe, B., Fabisch, A., De Maio, A., Oumer, N., Bissonnette, V., Marton, Z.-C., Kottath, S., Nissler, C., Yan, X., Trieble, R., Nuzzolo, F. (2017).
InFuse: A Comprehensive Framework for Data Fusion in Space Robotics.
In 14th Symposium on Advanced Space Technologies in Robotics and Automation.
Preprint
Fabisch, A., Kassahun, Y., Wöhrle, H., Kirchner, F. (2013).
Learning in Compressed Space.
Neural Networks, 42, 83–93.
DOI: 10.1016/j.neunet.2013.01.020
ScienceDirect Preprint Code
Kassahun, Y., Wöhrle, H., Fabisch, A., Tabie, M. (2012).
Learning Parameters of Linear Models in Compressed Parameter Space.
In A. E. P. Villa, W. Duch, P. Erdi, F. Masulli & G. Palm (Eds.), Proceedings of the 22nd International Conference on Artificial Neural Networks and Machine Learning (pp. 108–115). Springer.
DOI: 10.1007/978-3-642-33266-1_14
Springer
Wehbe, B., Fabisch, A., Krell, M. M. (2017).
Online Model Identification for Underwater Vehicles through Incremental Support Vector Regression.
In H. Zhang & R. Vaughan (Eds.), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4173-4180). IEEE.
DOI: 10.1109/IROS.2017.8206278
IEEE
Röfer, T., Laue, T., Müller, J., Fabisch, A., Feldpausch, F., Gillmann, K., Graf, C., de Haas, T. J., Härtl, A., Humann, A., Honsel, D., Kastner, P., Kastner, T., Könemann, C., Markowsky, B., Riemann, O. J. L., Wenk, F. (2011).
B-Human Team Report and Code Release 2011.
PDF
Röfer, T., Laue, T., Müller, J., Fabisch, A., Gillmann, K., Graf, C., Härtl, A., Humann, A., Wenk, F. (2011).
B-Human Team Description for RoboCup 2011.
In T. Röfer, N. M. Mayer, J. Savage and U. Saranli (Eds.), RoboCup 2011: Robot Soccer World Cup XV Preproceedings.
PDF
Fabisch, A., Laue, T., Röfer, T. (2010).
Robot Recognition and Modeling In the RoboCup Standard Platform League.
In E. Pagello, C. Zhou, S. Behnke, E. Menegatti, T. Röfer and P. Stone (Eds.), Proceedings of the Fifth Workshop On Humanoid Soccer Robots (Humanoids).
PDF
Röfer, T., Laue, T., Müller, J., Burchardt, A., Damrose, E., Fabisch, A., Feldpausch, F., Gillmann, K., Graf, C., de Haas, T. J., Härtl, A., Honsel, D., Kastner, P., Kastner, T., Markowsky, B., Mester, M., Peter, J., Riemann, O. J. L., Ring, M., Sauerland, W., Schreck, A., Sieverdingbeck, I., Wenk, F., Worch, J.-H. (2010).
B-Human Team Report and Code Release 2010
PDF
Röfer, T., Laue, T., Graf, C., Kastner, T., Fabisch, A., Thedieck, C. (2010).
B-Human Team Description For RoboCup 2010.
In E. Chown, A. Matsumoto, P. Ploeger, Ruiz–del–Solar, J. (Eds.), RoboCup 2010: Robot Soccer World Cup XIV Preproceedings.
PDF
Fabisch, A. (2023).
Transformations in Three Dimensions.
At EuroSciPy 2023
Abstract
Michalec, R., Martinez-Gonzalez, X., Fabisch, A. (2019).
"It works on my machine" - working as a research software engineer in a multi-partner international research project.
At RSEConUK 2019
Abstract Slides
Fabisch, A. (2020).
Learning and generalizing behaviors for robots from human demonstration
[Doctoral dissertation, University of Bremen], DOI: 10.26092/elib/382
SuUB Bremen
LaTeX (without images and literature)
Slides (without backup slides)
Fabisch, A. (2012).
Learning in Compressed Space (German: Lernen im komprimierten Raum)
[Diploma thesis, University of Bremen].
PDF
I have been interviewed for a study by Fraunhofer-Gesellschaft about machine learning research in Germany and relevant fields of research. It seems like I have an unusual view on machine learning because I work in a robotics institute. Although I do not agree with all of the results, some of my suggestions have been considered. The study is available for download here.
I maintain a quite popular answer at Stack Overflow that contains links to the most popular open source libraries for neural networks: link.
I have also been mentioned in acknowledgements of the following publications: