We develop technical systems that functionally support their users and provide them with a positive experience.Our research approach equally considers human factors and technical requirements based on a mixture of methods from engineering and human sciences. We demonstrate our findings on wearable systems such as prostheses or exoskeletons, cognitive systems such as collaborative or humanoid robots, and general applications with tight human-robot interaction.
Research projects
Fault diagnosis and tolerance for elastic actuation systems in robotics: physical human-robot interaction
Active transfer learning with neural networks through human-robot interactions (TRAIN)
EFFENDI – EFficient and Fast text ENtry for persons with motor disabilities of neuromuscular origin
Current projects
Learning Predictive Maintenance of Fleets of Networked Systems
(Third Party Funds Single)
Term: 1. September 2023 - 31. August 2026 Funding source: Bayerische Forschungsstiftung
The project aims at advancing predictive maintenance for networked device fleets using learning approaches and integrating expert knowledge. To this end, we will combine machine learning with physical models and analyze data flows between systems as well as integrate expertise on system behavior and failure modes. The resulting predictive maintenance approach for networked systems will be transferred to various classes of systems. Besides investigating industrial applications, we will create a fleet of mobile robots to demonstrate the capabilities of the predictive maintenance approach and make it available to academia, industry, and beyond.
Ferguson, N., Cansev, M.E., Dwivedi, A., & Beckerle, P. (2023). Design of a Wearable Haptic Device to Mediate Affective Touch with a Matrix of Linear Actuators. In Maurizio Valle, Dirk Lehmhus, Christian Gianoglio, Edoardo Ragusa, Lucia Seminara, Stefan Bosse, Ali Ibrahim, Klaus-Dieter Thoben (Eds.), Lecture Notes in Networks and Systems (pp. 507-517). Genova, ITA: Springer Science and Business Media Deutschland GmbH.
Beckerle, P. (2022). Wearable Robots Benchmarking: Comprehending and Considering User Experience. In Juan C. Moreno, Jawad Masood, Urs Schneider, Christophe Maufroy, Jose L. Pons (Eds.), Wearable Robotics: Challenges and Trends. (pp. 591-595). Cham: Springer Science and Business Media Deutschland GmbH.
Velasco Guillen, R.J., Furnemont, R., Verstraten, T., & Beckerle, P. (2022). A Stiffness-Fault-Tolerant Control Strategy for a Redundant Elastic Actuator. In IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (pp. 1360-1365). Sapporo, JPN: Institute of Electrical and Electronics Engineers Inc..
Denz, R., Demirci, R., Cansev, M.E., Bliek, A., Beckerle, P., Rueckert, E., & Rottmann, N. (2021). A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning. In 2021 20TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS (ICAR) (pp. 1109-1115). , ELECTR NETWORK: NEW YORK: IEEE.
Pott, P.P., Beckerle, P., & Stewart, K.W. (2021). Design and Hardware Integration of Elastic Actuators for HMI. In Philipp Beckerle, Maziar Ahmad Sharbafi, Tom Verstraten, Peter P. Pott, André Seyfarth (Eds.), Novel Bioinspired Actuator Designs for Robotics. (pp. 29-44). Springer Science and Business Media Deutschland GmbH.
Seiler, J., Schäfer, N., Latsch, B., Chadda, R., Hessinger, M., Kupnik, M., & Beckerle, P. (2020). Wearable vibrotactile interface using phantom tactile sensation for human-robot interaction. In Ilana Nisky, Jess Hartcher-O’Brien, Michaël Wiertlewski, Jeroen Smeets (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 380-388). Leiden, NL: Springer Science and Business Media Deutschland GmbH.
Velasco-Guillen, R.J., Grosu, V., Carmona-Ortiz, V.A., Vanderborght, B., Lefeber, D., Font-Llagunes, J.M., & Beckerle, P. (2020). A Stiffness-Fault-Tolerant Control Strategy for an Elastically Actuated Powered Knee Orthosis. In Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (pp. 660-665). New York City, NY, US: IEEE Computer Society.
We develop technical systems that functionally support their users and provide them with a positive experience.Our research approach equally considers human factors and technical requirements based on a mixture of methods from engineering and human sciences. We demonstrate our findings on wearable systems such as prostheses or exoskeletons, cognitive systems such as collaborative or humanoid robots, and general applications with tight human-robot interaction.
Research projects
Current projects
Learning Predictive Maintenance of Fleets of Networked Systems
(Third Party Funds Single)
Funding source: Bayerische Forschungsstiftung
The project aims at advancing predictive maintenance for networked device fleets using learning approaches and integrating expert knowledge. To this end, we will combine machine learning with physical models and analyze data flows between systems as well as integrate expertise on system behavior and failure modes. The resulting predictive maintenance approach for networked systems will be transferred to various classes of systems. Besides investigating industrial applications, we will create a fleet of mobile robots to demonstrate the capabilities of the predictive maintenance approach and make it available to academia, industry, and beyond.
Recent publications
2023
2022
2021
2020
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