http://dl.gi.de/handle/20.500.12116/36218 2026-07-21T13:23:43Z http://dl.gi.de/handle/20.500.12116/36261 News 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36259 Efficient Supervision for Robot Learning Via Imitation, Simulation, and Adaptation Wulfmeier, Markus Recent successes in machine learning have led to a shift in the design of autonomous systems, improving performance on existing tasks and rendering new applications possible. Data-focused approaches gain relevance across diverse, intricate applications when developing data collection and curation pipelines becomes more effective than manual behaviour design. The following work aims at increasing the efficiency of this pipeline in two principal ways: by utilising more powerful sources of informative data and by extracting additional information from existing data. In particular, we target three orthogonal fronts: imitation learning, domain adaptation, and transfer from simulation. 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36263 An Introduction to Hyperdimensional Computing for Robotics Neubert, Peer; Schubert, Stefan; Protzel, Peter Hyperdimensional computing combines very high-dimensional vector spaces (e.g. 10,000 dimensional) with a set of carefully designed operators to perform symbolic computations with large numerical vectors. The goal is to exploit their representational power and noise robustness for a broad range of computational tasks. Although there are surprising and impressive results in the literature, the application to practical problems in the area of robotics is so far very limited. In this work, we aim at providing an easy to access introduction to the underlying mathematical concepts and describe the existing computational implementations in form of vector symbolic architectures (VSAs). This is accompanied by references to existing applications of VSAs in the literature. To bridge the gap to practical applications, we describe and experimentally demonstrate the application of VSAs to three different robotic tasks: viewpoint invariant object recognition, place recognition and learning of simple reactive behaviors. The paper closes with a discussion of current limitations and open questions. 2019-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36262 Special Issue on Reintegrating Artificial Intelligence and Robotics Pecora, Federico; Mansouri, Masoumeh; Hawes, Nick; Kunze, Lars 2019-01-01T00:00:00Z