Angewandte Technisch-Kognitive Systeme/en

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= Applied Technical Cognitive Systems=



The research focuses on technologies of applied machine intelligence. Based on fundamental research new systems such as autonomous service robots, autonomous vehicles or assistance systems with cognitive capabilities are to be realized. The use of these so-called technical-cognitive systems takes place primarily in the context of highly automated, efficient and intermodal mobility; connected, automated production and logistics as well as the interactive support of the user in everyday situations.

Perception, situation assessment as well as decision making are the primarily addressed basics of machine intelligence. Methods for machine learning and probabilistic inference are thereby researched and applied for all components. The holistic use of neural methods in the areas of adaptive perception and behavioral decision making is being accounted for in the long term with the newly formed research focus of neurorobotics. Procedures for system evaluation and validation form an additional focus in the context of applied research. Autonomous vehicles like CoCar and CoCar-Zero, mobile robots such as the assistant robot Hollie, the walking robot Lauron or the inspection robot Cairo thereby form valuable integration and evaluation platforms for applied research. They are developed in close cooperation with the FZI and used for joint research and teaching.

2020-09-21: Best Dissertation Award - IEEE ITS Society
2018-11-15: Audi Autonomous Driving Cup 2018: Team AlpaKa wins the title
2018-11-05: Best Paper Award - IEEE International Conference on Intelligent Transportation Systems (ITSC)
2018-06-28: Best Paper Award - IEEE Intelligent Vehicles Symposium (IV)
2018-06-28: Best Paper Award - IEEE Intelligent Vehicles Symposium (IV)

Open Thesis Projects

Titel: Deep Reinforcement Learning for Autonomous Driving
Betreuer: Daniel Bogdoll
Abschlussarbeitstyp: Master

Titel: Thinking Fast and Slow with Model-Based Reinforcement Learning
Betreuer: Mohammd Karam Daaboul
Abschlussarbeitstyp: Master

Titel: Deep Reinforcement Learning for the Control of Robotic Manipulation
Betreuer: Mohammd Karam Daaboul
Abschlussarbeitstyp: Master

Titel: Efficient Uncertainty Aware Latent Model-Based Optimization
Betreuer: Mohammd Karam Daaboul
Abschlussarbeitstyp: Master

Titel: Machine Learning & Transfer Learning im Bereich der Arbeitsmaschinen
Betreuer: Mohammd Karam Daaboul
Abschlussarbeitstyp: Bachelor, Master

Titel: Manöver- / Trajektorienplanung unter Unsicherheiten
Betreuer: Philip Schörner
Abschlussarbeitstyp: Bachelor, Master

Open Positions

Student Assistant | Cooperatively Interacting Automobiles

Student Assistant | Systemadministration

Research Scientist | Highly Automated Driving

Our Partner Institutes
Forschungsbereich ISPE



Active Projects


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SPP 1835: Kooperativ interagierende Automobile
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Testfeld Autonomes Fahren
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