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Logo: Institute of Automatic Control
Logo Leibniz Universität Hannover
Logo: Institute of Automatic Control
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Human Motor Control

  • Modeling of the musculoskeletal system
  • Human motion generation and neuromotor control
  • Prostheses and neural controlled assistant systems

Neurally controlled prosthesis and assistance systems

The application of mechatronics, robotics and control theory for problems such as novel intelligent assistant systems, especially neurally controlled prostheses and assistance robots is a major research focus at IRT. For example, the first experiments with a paraplegic person who continuously controlled a robot via neural signals only were successfully conducted during Prof. Haddadin's time at DLR. Following an initial simulation test phase of approximately 4 years neural control, a context sensitive task planner, and a safety concept were designed and implemented for the experimental phase of the project. Thereafter, the participant was able to guide a torque-controlled DLR lightweight robot equipped with a five-finger hand for fine manipulation.





Haddadin, S. (2014): Towards Safe Robots: Approaching Asimov's 1st Law, Springer Publishing Company, Incorporated.
DOI: 10.1007/978-3-642-40308-8


Probol, T. (2000): Zur Synthese Nichtlinearer Regelgesetze mit Neuronalen Reglern, VDI-Fortschrittberichte, no. 819 der Reihe 8, Düsseldorf, VDI-Verlag. Dissertation, Universität Hannover.


Wendt, K. (1996): Modellierung des menschlichen Atmungssystems, VDI-Fortschrittberichte, no. 133 der Reihe 17, Düsseldorf, VDI-Verlag. Dissertation, Universität Hannover.



Povse, B.; Haddadin, S.; Belder, R.; Koritnik, D. & Bajd, T. (2015): A Tool for Evaluation of Human Lower Arm Injury: Approach, Experimental Validation and Application to Safe Robotics, Robotica
DOI: 10.1017/S0263574715000156

Vogel, J., Haddadin, S., Jarosiewicz, B., Simeral, J.D., Bacher, D., Hochberg, L.R., Donoghue, J.P., van der Smagt, P. (2015): An Assistive Decision and Control Architecture for Force-Sensitive Hand-Arm Systems driven via Human-Machine Interfaces, The International Journal of Robotics Research, vol. 34 no. 6, 763-780
DOI: 10.1177/0278364914561535


Conference Papers

Hu, Tingli; and Kühn, Johannes; and Ma'touq, Jumana & Haddadin, Sami (2018): Learning and Identification of human upper-limb muscle synergies in daily-life tasks with autoencoders, OTWorld Congress, Leipzig, Germany, 15.-18. May, more
DOI: 10.15488/3679