Ziel des vom BMBF geförderten Verbundvorhabens Sim4Dexterity ist die Erzeugung synthetischer Daten für die Robotermanipulation mit hochwertigen, interaktiven und validierbaren Simulationswerkzeugen, so dass benötigte KI-Lösungen wirtschaftlich und zeiteffizient realisiert werden können.
Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities.
The visual detection and recognition of human actions by technical systems is a fundamental problem with many applications the human-computer interaction domain. Activities involving hand-object interactions and action sequences in goal-oriented tasks, such as manufacturing work, pose a particular challenge. We use deep learning to detect and recognize such actions in real-time, and we propose an assistance system that provides the user with feedback and guidance based on the recognized actions.
Computer Vision and processing of multimodal sensor data is very important to take Smart Homes to the next level. An intelligent everyday environment should be aware of its residents. It should understand their actions and ideally even be able to predict their behavior. In the KogniHome project, we are developing computer vision and signal processing methods for three demonstrators: (1) KogniChef, a cognitive cooking assistant, (2) KogniMirror, a human aware smart mirror and (3) KogniDoor, an intelligent entrance door.
In Bielefeld, work is carried out on a bimanual anthropomorphic platform including the torso BARTHOC as a communication partner. We study interactive robot learning within a object learning scenario, i.e. labeling, grasping, and removing objects, aiming at a more natural human-robot cooperation. In particular, our research focuses on
bimanual action representation and execution
tactile sensors and manipulation based on tactile feedback
online-learning object detection
integration and coordination of perception and action
principles of human-robot dialog, including non-verbal communication
The spatio-temporal contact pattern during manipulation is a valuable source of information about object identity and object state, especially in uncertain environments. Using a bimanual robot manipulator setup with two 256 "pixel" touch sensor arrays, the present project is creating a "haptic pattern database" and investigates machine learning techniques to analyse the information contents of different haptic features and to extract identity and state information from haptic patterns. A closely connected goal are dynamic control strategies for contact movements with deformable or plastic objects, such as clay.
Ziel des vom BMBF geförderten Verbundvorhabens Sim4Dexterity ist die Erzeugung synthetischer Daten für die Robotermanipulation mit hochwertigen, interaktiven und validierbaren Simulationswerkzeugen, so dass benötigte KI-Lösungen wirtschaftlich und zeiteffizient realisiert werden können.
Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities. 
The visual detection and recognition of human actions by technical systems is a fundamental problem with many applications the human-computer interaction domain. Activities involving hand-object interactions and action sequences in goal-oriented tasks, such as manufacturing work, pose a particular challenge. We use deep learning to detect and recognize such actions in real-time, and we propose an assistance system that provides the user with feedback and guidance based on the recognized actions.
Computer Vision and processing of multimodal sensor data is very important to take Smart Homes to the next level. An intelligent everyday environment should be aware of its residents. It should understand their actions and ideally even be able to predict their behavior. In the 
The 
In Bielefeld, work is carried out on a bimanual anthropomorphic platform including the torso BARTHOC as a communication partner. We study interactive robot learning within a object learning scenario, i.e. labeling, grasping, and removing objects, aiming at a more natural human-robot cooperation. In particular, our research focuses on 
The spatio-temporal contact pattern during manipulation is a valuable source of information about object identity and object state, especially in uncertain environments. Using a bimanual robot manipulator setup with two 256 "pixel" touch sensor arrays, the present project is creating a "haptic pattern database" and investigates machine learning techniques to analyse the information contents of different haptic features and to extract identity and state information from haptic patterns. A closely connected goal are dynamic control strategies for contact movements with deformable or plastic objects, such as clay.