Many product innovations can benefit from being more adaptive to their environment. Learning adaptive behavior can be time sensitive and complex. For that purpose we created a simulation environment with a reinforcement learning component for developing simple adaptive products. The environment models …

The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot …

Key to reinforcement learning in multi-agent systems is the ability to exploit the fact that agents only directly influence only a small subset of the other agents. Such loose couplings are often modelled using a graphical model: a coordination graph. Finding an (approximately) optimal joint action for …

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase …

Dutch universities of applied sciences (UASs) had been teaching-only institutes since their legal origin in 1960. The development of higher education (HE) in Europe in the past twenty years requires UASs to embody and become hybrid organ-isations where education and research are integrated. Ever-changing, …

Autonomously exploring and mapping is one of the open challenges of robotics and artificial intelligence. Especially when the environments are unknown, choosing the optimal navigation directive is not straightforward. In this paper, we propose a reinforcement learning framework for navigating, exploring, …

Autonomous robots require high degrees of cognitive and motoric intelligence to come into our everyday life. In non-structured environments and in the presence of uncertainties, such degrees of intelligence are not easy to obtain. Reinforcement learning algorithms have proven to be capable of solving …

Het eerste doel is het evalueren en verbeteren van machine learning algoritmes voor insulineadviezen voor jongeren met diabetes type 1. Het tweede doel is het ontwikkelen van een kennisbank met geanonimiseerde datasets ten behoeve van de opleiding HBO-ICT. Studenten kunnen hiermee praktijkervaring opdoen …

Artificially intelligent agents increasingly collaborate with humans in human-agent teams. Timely proactive sharing of relevant information within the team contributes to the overall team performance. This paper presents a machine learning approach to proactive communication in AI-agents using contextual …

Insight into current scientific applications of Big Data in the precision dairy farming area may help us to understand the inflated expectations around Big Data. The objective of this invited review paper is to give that scientific background and determine whether Big Data has overcome the peak of inflated …