Welcome to the Chair of Information-Oriented Control!
We focus on data-driven and learning control as well as cooperative and networked control systems. We develop novel methods and tools for the formal analysis and robust control with guarantees taking into account uncertainties as well as limitations pertaining to acquisition of data, communication, and computation.
We apply our methods mainly to human-machine interaction, healthcare robotics, multi-robot systems, and general robotics. While our core competence is control engineering, we have interdisciplinary collaborations with the fields of machine learning, neuroscience (in human-robot interaction) and communications (in networked control systems).
Combination of a robotic system and FES for stroke rehabilitation
Actuation of human muscles using functional electrical stimulation
Robotic cooperative object mainpulation
Bio-inspired autonomous underwater vehicle
MuRoLa - Multi-robot laboratory
WearHap - Balancing experiment
Simulated obstacle detection with an artificial lateral line system
Deformable object manipulation
Cooperative robotics: Mounting a luggage rack
Robotic assistant: Mounting a bumper
Motion capturing system for mobile manipulation
Data-Driven based Control
Impedance-based Gaussian processes for human motor behavior models
Embodiment under autonomous control
Robot team teleoperation for cooperative manipulation task through wearable haptic devices