The research project CO-MAN aims to develop a novel framework for user-adaptive data-driven control with performance guarantees in order to address the scientific challenges of high uncertainty andi ndividual user requirements.
It is important for advances in technology to support human activities and interactions in the areas of healthcare, mobility and infrastructure systems. For instance, making healthcare more human requires digital interfaces to allow for more human interactions with the system. This is the goal of human-centric systems in which the human is both an element of the control system and a design criterion. The EU-funded CO-MAN project will develop a framework for user-adaptive data-driven control with performance guarantees. The biggest challenge will be to merge probabilistic non-parametric modelling techniques from statistical learning theory with novel risk-aware control methodologies while including active user modelling. The game changer is the current push towards reliable machine learning with novel results on theoretical bounds for learning behaviour.
- Identification and Control with Gaussian Processes
- Optimal Learning Control based on Gaussian Processes
- Intention estimation during physical Human-Robot-Interaction
- Estimation of human arm impedance for motor behavior models
- Embodiment under autonomous control
- Shared control for human-robot team interaction
- Neuroscientific models of Human-Human and Human-Robot Interaction
- Adaptive torque observer for human-exoskeleton interaction
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- How Training Data Impacts Performance in Learning-based Control. IEEE Control Systems Letters 5 (3), 2020, 905-910 more… BibTeX
- High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks. Scientific Reports 10 (1), 2020, 5860 more… BibTeX
- Data Selection for Multi-Task Learning Under Dynamic Constraints. IEEE Control Systems Letters 5 (3), 2020, 959-964 more… BibTeX