Intelligent Autonomous Systems

The Intelligent Autonomous Systems group investigates methods for cognition-enabled robot control. The research is at the intersection of robotics and Artificial Intelligence and includes methods for intelligent perception, dexterous object manipulation, plan-based robot control, and knowledge representation for robots.

Robots performing complex tasks in open domains, such as assisting humans in a household or collaboratively assembling products in a factory, need to have cognitive capabilities for interpreting their sensor data, understanding scenes, selecting and parametrizing their actions, recognizing and handling failures and interacting with humans. In our research, we are developing solutions for these kinds of issues and implement and test them on the robots in our laboratory. A particular focus of the group is on the integration of individual methods into complete cognition-enabled robot control systems and the release of the developed software as open-source libraries.

Collaborative Projects

RoboHow enables robots to competently perform everyday human-scale manipulation activities - both in human working and living environments.
SAPHARI investigates Safe and Autonomous Physical Human-Aware Robot Interaction
SHERPA develops a mixed autonomous ground and aerial robotic platform for support in search and rescue
ACAT enables robots to use information sources made for humans by learning and executing Action Categories
RoboSherlock poses perception as a question answering problem and uses the unstructured information management paradigm to create a framework for perceiving objects of daily use
BayCogRob -- Bayesian cognitive robotics - Autonomes Lernen für Bayes'sche kognitive Robotik (Schwerpunktprogramm Autonomes Lernen - DFG)
MeMoMan: Markerless Tracking of Human Motions investigates Methods for real-time accurate Model-based Measurement of HuMan Motion

Internal Research Projects

openEASE: Web-based Knowledge Processing Service for Robots and Robotics/AI Researchers
CRAM: Cognitive Robot Abstract Machine
KnowRob: Knowledge processing for autonomous robots
pracmln: Markov logic networks in Python
PRAC: Probabilistic Action Cores – Natural-language understanding for intelligent robots
Naive Physics Reasoning by Simulation: Simulation-based reasoning about physical effects

Prof. Dr. hc. Michael Beetz PhD
Head of Institute

Contact via
Andrea Cowley
assistant to Prof. Beetz

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