Künstliche Intelligenz 33(4) - Dezember 2019

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  • Zeitschriftenartikel
    Categorisations: AI
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Timpf, Sabine
  • Zeitschriftenartikel
    An Introduction to Hyperdimensional Computing for Robotics
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) 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.
  • Zeitschriftenartikel
    Benchmarking Functionalities of Domestic Service Robots Through Scientific Competitions
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Basiri, Meysam; Piazza, Enrico; Matteucci, Matteo; Lima, Pedro
    Benchmarking via carefully designed competitions makes it possible to provide a common framework for the rigorous comparison of intelligent and autonomous systems; competitions may play the role of scientific experiments while being appealing both to researchers and to the general public thus promoting critical analysis of systems outside the labs. This paper describes our approach to benchmarking domestic service robots through organizing recurrent competitions under the European Robotics League. It details the tools and benchmarks designed to evaluate the performance of robots at task and functionality levels. In particular, the functionality benchmarks for object perception and navigation are described and an overview of the new benchmarks to appear in the league is presented.
  • Zeitschriftenartikel
    Special Issue on Reintegrating Artificial Intelligence and Robotics
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Pecora, Federico; Mansouri, Masoumeh; Hawes, Nick; Kunze, Lars
  • Zeitschriftenartikel
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019)
  • Zeitschriftenartikel
    Efficient Supervision for Robot Learning Via Imitation, Simulation, and Adaptation
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) 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.
  • Zeitschriftenartikel
    Shakey Ever After? Questioning Tacit Assumptions in Robotics and Artificial Intelligence
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Kirsch, Alexandra
    Shakey the robot was a milestone of autonomous robots and artificial intelligence. Its design principles have dominated research until now. Tacit philosophical and architectural assumptions have impoverished the space of research topics and methods. I point out ways to overcome this impasse with sideglances to other scientific fields.
  • Zeitschriftenartikel
    Volvo Group Collaborative Robot Systems Laboratory: A Collaborative Way for Academia and Industry to be at the Forefront of Artificial Intelligence
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Götvall, Per-Lage
    With the clear aim of being on the forefront in the area of collaborative robots (cobots), the Volvo Group Truck Operations has created a collaboration arena where academia, industry, and start-up companies can share visions and needs and jointly create research and/or technology development projects. The arena has been active for about 5 years and is highly appreciated and beneficial for all involved parties. Advanced prototypes of robots and novel control methods for the usage of collaborative robots in an environment where human operators and robots are acting and collaborating on “equal terms” have been developed.
  • Zeitschriftenartikel
    A Philosophically Motivated View on AI and Robotics
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Kunze, Lars; Sloman, Aaron
  • Zeitschriftenartikel
    Weed Management of the Future
    (KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Amend, Sandra; Brandt, David; Di Marco, Daniel; Dipper, Tobias; Gässler, Gabriel; Höferlin, Markus; Gohlke, Maurice; Kesenheimer, Katharina; Lindner, Peter; Leidenfrost, Roland; Michaels, Andreas; Mugele, Tobias; Müller, Arthur; Riffel, Tanja; Sampangi, Yeshwanth; Winkler, Jan
    The methods used to protect agricultural products currently undergo drastic changes. Artificial Intelligence is a prime candidate to overcome two challenges faced by farmers around the world: The increasing cost and decreasing availability of human labor for weed control, and the growing global restriction of herbicides. Deep Learning is one of the most prominent approaches for applying AI to all kinds of use cases in industrial applications, entertainment, and security. Its latest field of application is plant classification that enables automated weed control and precise spot spraying of herbicides. While cheap, powerful platforms for deploying classification mechanisms are widely available, this comes at the cost of expensive and effort rich classifier training. This effectively makes Deep Learning-based approaches unavailable for the majority of the agricultural sector. Deepfield Robotics presents a systematic approach for deploying AI onto fields at large, including the learnings that led to their self-contained AI driven plant classification modules that relieve individuals from having to deploy their own AI solution. The same technology acts as enabler for more agricultural domains, such as targeted fertilization, nano irrigation, and automated phenotyping. This article documents Deepfield Robotics’ findings and vision on how AI can be the workhorse for agricultural weeding labor.