Heiner Markert

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Using associative memories and sparse distributed representations we have developed a system that can learn to associate words with objects, properties like colors, and actions. This system is used in a robotics context to enable a robot to respond to spoken commands like " bot show plum " or " bot put apple to yellow cup ". The scenario for this is a robot(More)
In this paper, the problem of safe exploration in the active learning context is considered. Safe exploration is especially important for data sampling from technical and industrial systems, e.g. combustion engines and gas turbines, where critical and unsafe measurements need to be avoided. The objective is to learn data-based regression models from such(More)
The brain representations of words and their referent actions and objects appear to be strongly coupled neuronal assemblies distributed over several cortical areas. In this work we describe the implementation of a cell assembly-based model of several visual, language, planning, and motor areas to enable a robot to understand and react to simple spoken(More)
We have implemented a neurobiologically plausible system on a robot that integrates visual attention, object recognition, language and action processing using a coherent cortex-like architecture based on neural associative memories. This system enables the robot to respond to spoken commands like " bot show plum " or " bot put apple to yellow cup ". The(More)
The throttle valve is a technical device used for regulating a fluid or a gas flow. Throttle valve control is a challenging task, due to its complex dynamics and demanding constraints for the controller. Using state-of-the-art throttle valve control, such as model-free PID controllers, time-consuming and manual adjusting of the controller is necessary. In(More)
Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single(More)
In this paper, we introduce a new and straightforward criterion for successive insertion and deletion of training points in sparse Gaussian process regression. Our novel approach is based on an approximation of the selection technique proposed by Smola and Bartlett [1]. It is shown that the resulting selection strategies are as fast as the purely randomized(More)