Lutz Frommberger

Learn More
A multitude of calculi for qualitative spatial reasoning (QSR) have been proposed during the last two decades. The number of practical applications that make use of QSR techniques is, however, comparatively small. One reason for this may be seen in the difficulty for people from outside the field to incorporate the required reasoning techniques into their(More)
The granularity of spatial calculi and the resulting mathematical properties have always been a major question in solving spatial tasks qualitatively. In this paper we present the Oriented Point Relation Algebra (OPRAm), a new orientation calculus with adjustable granularity. Since our calculus is a relation algebra in the sense of Tarski, fast standard(More)
Amultitude of calculi for qualitative spatial reasoning (QSR) has been proposed during the last two decades. The number of practical applications that make use of QSR techniques is, however, comparatively small. One reason for this may be seen in the difficulty for people from outside the field to incorporate the required reasoning techniques into their(More)
We present a learning approach for efficiently inducing adaptive behaviour of route instructions. For such a purpose we propose a two-stage approach to learn a hierarchy of wayfinding strategies using hierarchical reinforcement learning. Whilst the first stage learns low-level behaviour, the second stage focuses on learning high-level behaviour. In our(More)
The optimization of existing manufacturing systems is a challenging and highly complex task, requiring highquality information about the current system. Currently, acquiring such information involves tedious and to a large extend manual work. In this paper we present an ongoing joint project effort bringing together cognitive robotics and planning and(More)
The representation of the surrounding world plays an important role in robot navigation, especially when reinforcement learning is applied. This work uses a qualitative abstraction mechanism to create a representation of space consisting of the circular order of detected landmarks and the relative position of walls towards the agent’s moving direction. The(More)
Research on interactive systems and robots, i.e. interactive machines that <i>perceive, act</i> and <i>communicate</i>, has applied a multitude of different machine learning frameworks in recent years, many of which are based on a form of <i>reinforcement learning</i> (RL). In this paper, we will provide a brief introduction to the application of machine(More)
When a robot learns to solve a goal-directed navigation task with reinforcement learning, the acquired strategy can usually exclusively be applied to the task that has been learned. Knowledge transfer to other tasks and environments is a great challenge, and the transfer learning ability crucially depends on the chosen state space representation. This work(More)