Research Teams


Team: Humanoid Robotics

Humanoid robots are autonomous machines that resemble the human body. They provide a versatile body to develop cognitive systems to collaborate and help humans in daily life and work. Humanoid robots are complex in many senses. They are highly redundant floating-base-biped robots with free upper limbs for physical interaction. Their complex dynamics require the application of robust whole-body control methods to achieve even the most simple task.

At the humanoid robotics team, we research the control methods that whole-body tactile feedback allows for physical interaction and biped locomotion. Direct tactile feedback enables safe physical interaction between robots and dynamically changing environments. It helps detect contacts and their properties (fixation, friction, shape, and pressure distribution), not only for handling objects with the upper body but also for keeping balance while walking over unstructured, complex terrain.


Foto von Wenlan Shen

M.Sc. Wenlan Shen

Master students:

  • Aimilios Petroulas
  • Jiaxin Yang

Team: Neuroengineering

Neuroengineering is an emerging research field which is characterized by the synergistic combination of theories and methods from neuroscience and engineering.

Aitana Arranz Ibáñez, Master Student in Neuroengineering

 

 

An Binh Vu, Master Student in Electrical Engineering and Computer Science

 

 

Andreas Schwersenz, MBA Thesis Student

 

 

Jin Ho Lee, Master Student in Electrical Engineering and Computer Science

 

 

Karahan Yilmazer, Master Student in Neuroengineering

 

 

Maria-Luna Ghanime, Master Student in Neuroengineering

 

 

Christian Ritter, Master Student in Robotics, Cognition, Intelligence

 

 

Andres Rosa, Master Student in Electrical Engineering and Computer Science

 

 

Anna Polato, Exchange Master Student

 

  • Gordon Cheng, Stefan K. Ehrlich, Mikhail Lebedev and Miguel A. L. Nicolelis: Neuroengineering challenges of fusing robotics and neuroscience. Science Robotics (Vol. 5, Issue 49, eabd1911), 2020. DOI: 10.1126/scirobotics.abd1911
  • Natalia Paredes-Acuña; Nicolas Berberich; Emmanuel Dean-León; Gordon Cheng: Tactile-based Assistive Method to Support Physical Therapy Routines in a Lightweight Upper-Limb Exoskeleton. IEEE Transactions on Medical Robotics and Bionics, 2022 mehr… BibTeX Volltext ( DOI )
  • Nicolas Berberich*, Natalia Paredes-Acuña*, Benjamin Lipp, Gordon Cheng: Embedding Ethics into Neuroengineering Education: A Human-Centered Engineering Course on Neurorehabilitation, IEEE EMBS International Conference on Neural Engineering 2023 (accepted)
  • Laura Pilger*, Nicolas Berberich*, Natalia Paredes-Acuña, Adrian Dendorfer, Julio Rogelio Guadarrama-Olvera, Florian Bergner, Daniel Utpadel-Fischler, Gordon Cheng: Human-Centered Design of a Vibrotactile Sensory Substitution Belt for Feet Somatosensation in a Patient with Multiple Sclerosis, IEEE EMBS International Conference on Neural Engineering 2023 (accepted)
  • Nicholas Tacca*, John Nassour*, Stefan K. Ehrlich, Nicolas Berberich, Gordon Cheng: Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials. Journal of NeuroEngineering and Rehabilitation, 2022 mehr… BibTeX Volltext ( DOI )
  • Alireza Malekmohammadi, Stefan K Ehrlich, Gordon Cheng: Modulation of theta and gamma oscillations during familiarization with previously unknown music. Brain Research 1800, 2023 mehr… BibTeX

 

  • Biosignal Processing and Modeling
  • Neuro-inspired Systems Engineering
  • Practical Course Neural Signals
  • Human-Centered Neuroengineering: Neurorehabilitation
  • Human-Centered Neuroengineering: Cybathlon
  • Neuroengineering Symposium

Adrian Dendorfer (Bachelor Thesis in Engineering Science)

Laura Pilger (Research Internship, Master Electrical Engineering and Information Technology

Team: Cognitive Engineering - Purposive Learning 

Cognitive engineering refers to systems inspired by cognitive abilities such as reasoning, perception, learning, problem-solving, decision-making, and natural language processing. The goal is to further the understanding of human cognition and to create intelligent machines that enable intuitive human-centred interaction in domains such as everyday life tasks.

Simon Armleder

Nicolas Berberich

Katrin Schulleri

  • G. Cheng, K. Ramirez-Amaro, M. Beetz, and Y. Kuniyoshi, “Purposive learning: Robot reasoning about the meanings of human activities,” Sci. Robot., vol. 4, no. 26, p. eaav1530, Jan. 2019, doi: 10.1126/scirobotics.aav1530.
  • C. Uhde, N. Berberich, H. Ma, R. Guadarrama and G. Cheng, "Learning Causal Relationships of Object Properties and Affordances Through Human Demonstrations and Self-Supervised Intervention for Purposeful Action in Transfer Environments," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11015-11022, Oct. 2022, doi: 10.1109/LRA.2022.3196125
  • C. Uhde, N. Berberich, K. Ramirez-Amaro and G. Cheng, "The Robot as Scientist: Using Mental Simulation to Test Causal Hypotheses Extracted from Human Activities in Virtual Reality," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020, pp. 8081-8086, doi: 10.1109/IROS45743.2020.9341505

Lead:


Picture of John Nassour

Dr.-Ing. John Nassour

Team members:


kein Bild

M.Sc. Jin Ho Lee