CO-MAN: Safe data-driven control for human-centric systems

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 and individual user requirements.

Motivation

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.

Links

TUM team members

Selected publications

2023

  • A. Lederer; A. Begzadi; N. Das; S. Hirche: Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions. 2023, The 22nd World Congress of the International Federation of Automatic Control, 2023 more… BibTeX Full text (mediaTUM)
  • Das, Neha; Endo, Satoshi; Patel, Sabrina; Krewer, Carmen; Hirche, Sandra: Online detection of compensatory strategies in human movement with supervised classification: a pilot study. Frontiers in Neurorobotics 17, 2023 more… BibTeX Full text ( DOI )
  • H. Kavianirad; M. Forouhar; H. Sadeghian; S. Endo; S. Haddadin; S. Hirche: Model-Based Shared Control of a Hybrid FES-Exoskeleton: an Application in Participant-Specific Robotic Rehabilitation. 2023 International Conference on Rehabilitation Robotics, ICORR 2023, 2023 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • Li, Cong; Liu, Qingchen; Qin, Jiahu; Buss, Martin; Hirche, Sandra: Safe Planning and Control Under Uncertainty: A Model-Free Design With One-Step Backward Data. IEEE Transactions on Industrial Electronics 71 (1), 2023, 729-738 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • Li, Jiacheng; Liu, Qingchen; Jin, Wanxin; Qin, Jiahu; Hirche, Sandra: Robust Safe Learning and Control in an Unknown Environment: An Uncertainty-Separated Control Barrier Function Approach. IEEE Robotics and Automation Letters 8 (10), 2023, 6539-6546 more… BibTeX Full text ( DOI )
  • N. Das; J. Umlauft; A. Lederer; A. Capone; T. Beckers; S. Hirche: Deep Learning based Uncertainty Decomposition for Real-time Control. 2023, The 22nd World Congress of the International Federation of Automatic Control, 2023 more… BibTeX Full text (mediaTUM)
  • Omainska, Marco; Yamauchi, Junya; Lederer, Armin; Hirche, Sandra; Fujita, Masayuki: Rigid Motion Gaussian Processes With SE(3) Kernel and Application to Visual Pursuit Control. IEEE Control Systems Letters 7, 2023, 2665-2670 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • Römer, Ralf; Lederer, Armin; Tesfazgi, Samuel; Hirche, Sandra: Vision-Based Uncertainty-Aware Motion Planning Based on Probabilistic Semantic Segmentation. IEEE Robotics and Automation Letters 8 (11), 2023, 7825-7832 more… BibTeX Full text ( DOI ) Full text (mediaTUM)

2022

  • A. Capone; A. Lederer; S. Hirche: Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications. Proceedings of the 39th International Conference on Machine Learning, 2022 more… BibTeX Full text (mediaTUM)
  • A. J. Ordóñez-Conejo; A. Lederer; S. Hirche: Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes. Proceedings of the European Control Conference, 2022, 2234-2240 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • A. Lederer; M. Zhang; S. Tesfazgi; S. Hirche: Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes. Proceedings of the IEEE Conference on Control Technology and Applications, 2022 more… BibTeX Full text (mediaTUM)
  • A. Lederer; Z. Yang; J. Jiao; S. Hirche: Cooperative Control of Uncertain Multi-Agent Systems via Distributed Gaussian Processes. IEEE Transactions on Automatic Control, 2022 more… BibTeX Full text (mediaTUM)
  • G. Evangelisti; S. Hirche: Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes. 2022 IEEE 61st Conference on Decision and Control (CDC), IEEE, 2022 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • H. Kavianirad; S. Endo; T. Keller; S. Hirche: EMG-Based Volitional Torque Estimation in Functional Electrical Stimulation Control. 2022 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2022 more… BibTeX Full text (mediaTUM)
  • Jiao, Junjie; Capone, Alexandre; Hirche, Sandra: Backstepping tracking control using Gaussian processes with event-triggered online learning. IEEE Control Systems Letters, 2022, 3176 - 3181 more… BibTeX Full text (mediaTUM)
  • S. Curi; A. Lederer; S. Hirche; A. Krause: Safe Reinforcement Learning via Confidence-Based Filters. Proceedings of the IEEE Conference on Decision and Control, 2022 more… BibTeX Full text (mediaTUM)
  • T. Beckers; Leonardo J. Colombo; S. Hirche: SAFE TRAJECTORY TRACKING FOR UNDERACTUATED VEHICLES WITH PARTIALLY UNKNOWN DYNAMICS. AIMS Journal, 2022 more… BibTeX Full text (mediaTUM)
  • T. Beckers; S. Hirche: Prediction with Approximated Gaussian Process Dynamical Models. IEEE Transactions on Automatic Control (TAC) 2022, 2022 more… BibTeX Full text ( DOI ) Full text (mediaTUM)

2021

  • A. Lederer; A. J. Ordóñez Conejo; K. Maier; W. Xiao; J. Umlauft; S. Hirche: Gaussian Process-Based Real-Time Learning for Safety Critical Applications. Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research 139), 2021, 6055-6064 more… BibTeX Full text (mediaTUM)
  • Degue, Kwassi H.; Efimov, Denis; Le Ny, Jerome; Hirche, Sandra: Design of Interval Observers for Uncertain Linear Impulsive Systems. 2021 60th IEEE Conference on Decision and Control (CDC), IEEE, 2021 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • K. H. Degue; D. Efimov; J. Le Ny; S. Hirche: Novel_Interval_Observer_Hybrid_Systems_Final_Version. 2021 60th IEEE Conference on Decision and Control, 2021 more… BibTeX Full text (mediaTUM)
  • M. Omainska; J. Yamauchi; T. Beckers; T. Hatanaka; S. Hirche; M. Fujita: Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions. SICE Journal of Control, Measurement, and System Integration 14 (1), 2021, 116-127 more… BibTeX Full text ( DOI )
  • T. Beckers; S. Hirche: Prediction with Approximated Gaussian Process Dynamical Models. IEEE Transactions on Automatic Control , 2021 more… BibTeX Full text ( DOI )
  • Yamauchi, Junya; Omainska, Marco; Beckers, Thomas; Hatanaka, Takeshi; Hirche, Sandra; Fujita, Masayuki: Cooperative Visual Pursuit Control with Learning of Position Dependent Target Motion via Gaussian Process. 2021 60th IEEE Conference on Decision and Control (CDC), IEEE, 2021 more… BibTeX Full text ( DOI )

2020

  • A. Capone; G.Noske; J. Umlauft; T. Beckers; A. Lederer; S. Hirche: Localized active learning of Gaussian process state space models. Learning for Dynamics & Control, 2020 more… BibTeX Full text (mediaTUM)
  • A. Capone; A. Lederer; J. Umlauft; S. Hirche: Data Selection for Multi-Task Learning Under Dynamic Constraints. IEEE Control Systems Letters 5 (3), 2020, 959-964 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • A. Lederer; A. Capone; J. Umlauft; S. Hirche: How Training Data Impacts Performance in Learning-based Control. IEEE Control Systems Letters 5 (3), 2020, 905-910 more… BibTeX Full text ( DOI ) Full text (mediaTUM)
  • J. Umlauft; T. Beckers; A. Capone; A. Lederer; S. Hirche: Smart Forgetting for Safe Online Learning with Gaussian Processes. Learning for Dynamics & Control, 2020 more… BibTeX Full text (mediaTUM)
  • Pfister, Franz M. J.; Um, Terry Taewoong; Pichler, Daniel C.; Goschenhofer, Jann; Abedinpour, Kian; Lang, Muriel; Endo, Satoshi; Ceballos-Baumann, Andres O.; Hirche, Sandra; Bischl, Bernd; Kulić, Dana; Fietzek, Urban M.: High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks. Scientific Reports 10 (1), 2020, 5860 more… BibTeX Full text ( DOI ) Full text (mediaTUM)