Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in
dynamic environments based on feedback. Explicit human feedback during robot RL is …

Motivation: For deep learning on image data, a common approach is to augment the training
data by artificial new images, using techniques like moving windows, scaling, affine …

A challenging goal in human-robot interaction research is to build robots that are intuitive
interaction partners for humans. Although some research does focus on building robots …

Goal: Current brain-computer interfaces (BCIs) are usually based on various, often
supervised, signal processing methods. The disadvantage of supervised methods is the …

In the past, robots were primarily used to perform work that was either too hard, too
dangerous or simply too repetitive for humans, eg, assembly line work, or work that could be …

A current trend in the development of assistive devices for rehabilitation, for example
exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality …

Advanced man-machine interfaces (MMIs) are being developed for teleoperating robots at
remote and hardly accessible places. Such MMIs make use of a virtual environment and can …

This paper proposes an application oriented approach that enables to transfer a classifier
trained within an experimental scenario into a more complex application scenario or a …

In the presented research project, a software and hardware infrastructure for parking space
focussed inter-modal route planning in a public pilot region in Berlin is developed. One …

Objective. Classifier transfers usually come with dataset shifts. To overcome dataset shifts in
practical applications, we consider the limitations in computational resources in this paper …