Cooperative Systems

Cooperative Control Systems


How will automation interact with humans in the future?

How to create synergies between humans and machines in the context of Industry 4.0?

The research group Cooperative Systems develops a framework for modeling and control of interactions between humans and machines. The individual strengths of human and machine are combined to achieve high performance systems, ready to meet future challenges of automatization. The fields of applications are e.g. Advanced Driver Assistance Systems, Robotics, Medical Technologies and Aerospace Engineering.

Cooperative Control Loop

Cooperative Control Loop

Modeling and Identification

The modeling of cooperative systems forms the basis of automation design for cooperative scenarios. In this context, uncertainties in perception and action need to be considered. Furthermore, semantics enable a strategic description of the interaction. Moreover, the identification of human behavior is essential in automation design.

Control Synthesis

Automation design in a cooperative scenario needs to be capable of a dynamic allocation of authority. Furthermore, it requires the ability to negotiate a common goal with the human. One approach to control cooperative systems is based on game theory and Model Predictive Control (MPC). In order to achieve real-time control, motion primitives are examined.



Motion Tracking is used in various scenarios to measure human motion and validate identification methods.

An Advanced Driving Simulator with haptic feedback human machine interfaces was developed at the IRS. It is used to validate cooperative control methods in the context of advanced driver assistance systems.

A newly developed Ball-on-Plate experiment will be used to apply cooperative identification and control methods in a highly dynamical scenario.


Simon Rothfuß

Head of Research Group

Research Interest:
Modeling of Cooperative Goal Negotiation

Balint Varga

Research Associate

Research Interest:
Cooperative control of mobile machinery

Julian Schneider

Research Associate

Research Interest:
Multi level connection for the consistent design of cooperative human-machine systems

Philipp Karg

Research Associate

Research Interest:
Modeling and Identification in Cooperative Human-Machine Scenarios

Sean Kille

Research Associate

Research Interest:

Esther Bischoff

Research Associate

Research Interest:

Christian Braun

Research Associate

Research Interest:
Shared control of heterogenous robot swarms

Student Assistants

Manuel Hess

Betreuung des Fahrsimulators

Hongdong Zhao

Software development and experimental implementation in the field of multi-robot manufacturing system

Adrian Miecznikowski

Setting up a robot field



Recent job offers for student assistants can be found here.

Bachelor and Master Students

Tiancheng Yu

Master Thesis

Kooperative Regelung hochautomatisierter Fahrzeuge in gemischten Verkehrsszenarien

Ruoli Chen

Master Thesis

Modellierung von Multi-Agent Collision-Avoidance-Szenarien am Beispiel mobiler Roboterplattformen

Pedro Cerdano Vicente

Bachelor Thesis

Simulationsumgebung für die Mensch-Maschine-Kooperation

Zhenghong Li

Master Thesis

Entwurf und Implementierung eines LQ-Differentialspielreglers zur Untersuchung effizienter Kooperationsszenarien

Juan Sebastian Chica Munoz

Bachelor Thesis

Modellierung eines Roboterarms

Lars Fischer

Master Thesis

Modellierung und Identifikation von Kooperativen Mensch-Roboter-Systemen mit Adaptable Automation

Linus Witucki

Master Thesis

Untersuchung symbiotischer Mensch-Maschine-Interaktion auf Aktionsebene

David Erdelyi

Master Thesis

Kooperative Bahnplanung zwischen  Mensch und Roboter auf Basis der Spieltheorie

Annika Lang

Bachelor Thesis

Modellierung von Verhandlungssituationen in gemischten Verkehrsszenarien

Longwei Cong

Master Thesis

Effizientes Sampling-basiertes Inverse Reinforcement Learning

Daniela Hahn

Master Thesis

Entwicklung von Reoptimierungs-Methoden zur kooperativen Koordination heterogener Multi-Roboter-Teams

Tian Fang

Master Thesis

Entwicklung eines Moduls zur Erkennung von Bewegungsintentionen eines Menschen

Yuhan Jin

Master Thesis

Untersuchung und simulative Umsetzung von Regelungsmethoden zur garantierten Regelung zweier gekoppelter Roboter

Robin Fabian Wöran

Master Thesis

Implementierung eines exakten Koordinierungsalgorithmus für Multi-Roboter-Systeme

Zhanhao Liang

Master Thesis

Identifikation menschlicher Bewegungen mittels Inverse Reinforcement Learning

Maximilian Schwind

Master Thesis

Entwurf und Implementierung eines menschenzentrierten Reglers

Jonas Kaub

Master Thesis

Robustheitsanalyse und robustes Design inverser dynamischer Spiele

Manuel Hess

Master Thesis

Bi-level-based Inverse Stochastic Optimal Control