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Logo: Institute of Automatic Control
Logo Leibniz Universität Hannover
Logo: Institute of Automatic Control
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Machine Learning

  • Multi-agent reinforcement learning
  • Learning of optimal motions/trajectories
  • Generalization of optimal trajectories
  • Task learning and -adaption

Non-linear Control Theory and Machine Learning

The research at IRT connects modern non-linear and optimal control of complex dynamical systems with methods from machine learning. Central results and new approaches for the systematic combination of optimal control of complex non-linear systems and learning and generalization were developed based on extending state-of-the-art machine learning methods. The results were successfully validated in highly complex robotic applications, allowing to find and apply almost optimal solutions for highly non-linear problems in real-time.

Publications


Books

Höhn, O. (2008): Erkennung, Klassifikation und Vermeidung von Stürzen zweibeiniger Roboter, Norderstedt, Books On Demand. Dissertation, Leibniz Universität Hannover. more

BibTeX

Book Chapter

Haddadin, S., Weitschat, R., Huber, F., Özparpucu, M. C., Mansfeld, N., Albu-Schäffer, A. (2016): Optimal Control for Viscoelastic Robots and Its Generalization in Real-Time, Inaba, M., Corke, P. (Eds.): Robotics Research: The 16th International Symposium ISRR, Springer International Publishing, 131-148
DOI: 10.1007/978-3-319-28872-7_8

Journals

Li, Y. and Ganesh, G.; Jarrasse, N.; Haddadin, S.; Albu-Schäffer, A. & Burdet, E. (2018): Force, Impedance and Trajectory Learning for Contact Tooling and Haptic Identification, IEEE Transactions on Robotics, 34, 1170-1182
DOI: 10.1109/TRO.2018.2830405

Höhn, O. & Gerth, W. (2009): Probabilistic Balance Monitoring for Bipedal Robots, The International Journal of Robotics Research, vol. 28, no. 2, Feb, pp. 245-256.
DOI: 10.1177/0278364908095170

BibTeX

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

Diaz Ledezma, Fernando & Haddadin, Sami (2017): First-Order-Principles-Based Constructive Network Topologies: An Application to Robot Inverse Dynamics, IEEE RAS International Conference on Humanoid Robots, Birmingham, UK, 438-445
DOI: 10.1109/HUMANOIDS.2017.8246910

Golz, S., Osendorfer, Ch. & Haddadin, S. (2015): Using tactile sensation for learning contact knowledge: Discrimination collision from physical interaction, Accepted at: 2015 IEEE International Conference on Robotics and Automation
DOI: 10.1109/ICRA.2015.7139726

BibTeX

Höhn, O. & Gerth, W. (2008): Wahrscheinlichkeitsbasierte Sturzklassifikation von zweibeinigen Robotern, 42. Regelungstechnisches Kolloquium -- Kurzfassung der Beiträge, Feb., Boppard, pp. 39-40. more

BibTeX

Höhn, O., Schollmeyer, M. & Gerth, W. (2004): Sturzvermeidung von zweibeinigen Robotern durch reflexartige Reaktionen, In Holleczek, P. & Vogel-Heuser, B. (Ed.): Eingebettete Systeme. PEARL 2004, Informatik aktuell, Berlin Heidelberg, Springer, pp. 60-69. more
DOI: 10.1007/978-3-642-18594-6_7

BibTeX