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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 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)
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)
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)
We present a learning approach for efficiently inducing adap-tive 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)
Spatial abstraction empowers complex agent control processes. We propose a formal definition of spatial abstraction and classify it by its three facets, namely aspectualization, coarsening, and conceptual classification. Their characteristics are essentially shaped by the representation on which abstraction is performed. We argue for the use of so-called(More)
In robot navigation tasks, the representation of the surrounding world plays an important role, especially in reinforcement learning approaches. This work presents a qualitative representation of space consisting of the circular order of detected landmarks and the relative position of walls towards the agent's moving direction. The use of this(More)
— Locomotion of walking machines on a well defined path in unstructured terrain requires a model of the environment. But, in particular, middle sized robots like LAURON III don't provide the possibility to carry large or heavy sensors. This paper focuses on generating a 3D map of unstructured environment on the basis of sparse sensory information. In(More)