Search Results for author: Kyrre Glette

Found 12 papers, 4 papers with code

Complexity-based Encoded Information Quantification in Neurophysiological Recordings

no code implementations3 May 2022 Julian Fuhrer, Alejandro Blenkmann, Tor Endestad, Anne-Kristin Solbakk, Kyrre Glette

Here, we propose a method grounded in algorithmic information theory that affords direct statements about responses' similarity by estimating the encoded information through a compression-based scheme.

Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment

1 code implementation7 Apr 2021 Emma Hjellbrekke Stensby, Kai Olav Ellefsen, Kyrre Glette

We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments.

Quality and Diversity in Evolutionary Modular Robotics

no code implementations5 Aug 2020 Jørgen Nordmoen, Frank Veenstra, Kai Olav Ellefsen, Kyrre Glette

In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting search algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality Diversity algorithm, for the difficult problem of evolving control and morphology in modular robotics.

A Framework for Automatic Behavior Generation in Multi-Function Swarms

no code implementations11 Jul 2020 Sondre A. Engebraaten, Jonas Moen, Oleg A. Yakimenko, Kyrre Glette

This repertoire would enable the swarm to transition between behavior trade-offs online, according to the situational requirements.

On Restricting Real-Valued Genotypes in Evolutionary Algorithms

no code implementations19 May 2020 Jørgen Nordmoen, Tønnes Frostad Nygaard, Eivind Samuelsen, Kyrre Glette

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms.

Environmental Adaptation of Robot Morphology and Control through Real-world Evolution

no code implementations30 Mar 2020 Tønnes F. Nygaard, Charles P. Martin, David Howard, Jim Torresen, Kyrre Glette

We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments.

A Transferable Adaptive Domain Adversarial Neural Network for Virtual Reality Augmented EMG-Based Gesture Recognition

1 code implementation16 Dec 2019 Ulysse Côté-Allard, Gabriel Gagnon-Turcotte, Angkoon Phinyomark, Kyrre Glette, Erik Scheme, François Laviolette, Benoit Gosselin

The ability of the dynamic dataset to serve as a benchmark is leveraged to evaluate the impact of different recalibration techniques for long-term (across-day) gesture recognition, including a novel algorithm, named TADANN.

14 EMG Gesture Recognition +2

Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing

no code implementations15 Mar 2018 Tønnes F. Nygaard, Charles P. Martin, Jim Torresen, Kyrre Glette

This allows active adaptation of morphology to different environments, and enables rapid tests of morphology with a single robot.


Multi-objective Analysis of MAP-Elites Performance

no code implementations14 Mar 2018 Eivind Samuelsen, Kyrre Glette

In certain complex optimization tasks, it becomes necessary to use multiple measures to characterize the performance of different algorithms.

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

3 code implementations10 Jan 2018 Ulysse Côté-Allard, Cheikh Latyr Fall, Alexandre Drouin, Alexandre Campeau-Lecours, Clément Gosselin, Kyrre Glette, François Laviolette, Benoit Gosselin

Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.

EMG Gesture Recognition General Classification +2

Robot Localisation and 3D Position Estimation Using a Free-Moving Camera and Cascaded Convolutional Neural Networks

no code implementations6 Jan 2018 Justinas Miseikis, Patrick Knobelreiter, Inka Brijacak, Saeed Yahyanejad, Kyrre Glette, Ole Jakob Elle, Jim Torresen

This can be the case when sensors and the robot are calibrated in relation to each other and often the reconfiguration of the system is not possible, or extra manual work is required.

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