Leo Pape

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We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of(More)
Curiosity is an essential driving force for science as well as technology, and has led mankind to explore its surroundings, all the way to our current understanding of the universe. Space science and exploration is at the pinnacle of each of these developments, in that it requires the most advanced technology, explores our world and outer space, and(More)
— We introduce a novel framework that builds task-relevant roadmaps (TRMs). TRMs can be used to plan complex task-relevant motions on robots with many degrees of freedom. To this end we create a new sampling based inverse kinematics optimizer called Natural Gradient Inverse Kinematics (NGIK), based on the principled heuristic solver called natural evolution(More)
The ability to identify novel patterns in observations is an essential aspect of intelligence. In a computational framework, the notion of a pattern can be formalized as a program that uses regularities in observations to store them in a compact form, called a compressor. The search for interesting patterns can then be stated as a search to better compress(More)
—Humanoids have to deal with novel, unsupervised high-dimensional visual input streams. Our new method Au-toIncSFA learns to compactly represent such complex sensory input sequences by very few meaningful features corresponding to high-level spatio-temporal abstractions, such as: a person is approaching me, or: an object was toppled. We explain the(More)
The temporal evolution of nearshore sandbars (alongshore ridges of sand fringing coasts in water depths less than 10 m and of paramount importance for coastal safety) is commonly predicted using process-based models. These models are autoregressive and require offshore wave characteristics as input, properties that find their neural network equivalent in(More)
— Deep belief networks (DBNs) are popular for learning compact representations of high-dimensional data. However, most approaches so far rely on having a single, complete training set. If the distribution of relevant features changes during subsequent training stages, the features learned in earlier stages are gradually forgotten. Often it is desirable for(More)
In this thesis several possibilities are investigated for improving the performance of Liquid State Machines. A Liquid State Machine is a relatively new system that is a Machine Learning system, which is capable of coping with temporal dependencies. Basic Recurrent Neural Networks often have problems with this. One reason for this is that it takes a long(More)