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Veenstra, Frank; Norstein, Emma Stensby & Glette, Kyrre
(2023).
Tutorial: Evolving Robot Bodies and Brains with Unity.
Vis sammendrag
The evolution of robot bodies and brains allows researchers to investigate which building blocks are interesting for evolving artificial life. Agnostic to the evolutionary approach used, the supplied building blocks influence how artificial organisms will behave. What should these building blocks look like? How should we associate control units with these building blocks? How should we represent the genomes of these robots? In this tutorial we discuss (1) previous approaches to evolving robots and virtual creatures, (2) outline how Unity simulations and Unity's ML-agents package can be used as an interface, and (3) our approach to evolving bodies and brains using Unity.
There are many existing solutions that are tailored to experimenting with body brain co-optimization and we have been using several simulation approaches to evolve modular robots that are represented by directed trees (directed acyclic graphs). Since evolving bodies can be relatively complex, we give participants an overview of existing methods and invite the participants to get some guided hands-on experience using the Unity ML-Agents for evolving robots. The Unity ML-Agents toolkit is an open-source toolkit for game developers, AI researchers, and hobbyists that can be used to train agents using various AI methods. Similar to OpenAI gym, it supplies a Python API through which one can optimize agents in a variety of environments. The Unity ML-Agents toolkit provides an easy-to-use interface that is flexible enough to allow for quick design iterations for evolving robot bodies and brains.
This tutorial is aimed at researchers that are interested in simulating the evolution of bodies and brains of robots. The tutorial will provide an overview of existing approaches to evolving bodies and brains of robots, and demonstrate how to design and incorporate control units, morphological components, environments and objectives. Participants will learn how to use Unity ML-Agents as a tool with evolutionary algorithms and learn how they can create incorporate their own robotic modules for evolving robots.
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(2023).
Evolutionary and adaptive robotics: from simulation to reality
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(2023).
Bio-inspiration for robot design and adaptation
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(2023).
Adaptive robots through evolutionary algorithms and machine learning.
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Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia
[Vis alle 10 forfattere av denne artikkelen]
(2022).
Direct brain recordings reveal continuous encoding of structure in random stimuli.
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Fuhrer, Julian; Blenkmann, Alejandro Omar; Endestad, Tor; Solbakk, Anne-Kristin & Glette, Kyrre
(2022).
Complexity-Based Encoded Information Quantification in Neurophysiological Recordings.
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Szorkovszky, Alexander; Veenstra, Frank & Glette, Kyrre
(2022).
From real-time adaptation to social learning in robots.
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Fuhrer, Julian; Glette, Kyrre; Ivanovic, Jugoslav; Larsson, Pål Gunnar; Bekinschtein, Tristan & Kochen, Silvia
[Vis alle 10 forfattere av denne artikkelen]
(2022).
Direct brain recordings reveal continuous encoding of structure in random stimuli.
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Krzyzaniak, Michael Joseph; Gerry, Jennifer; Kwak, Dongho; Erdem, Cagri; Lan, Qichao & Glette, Kyrre
(2021).
Fibres Out of Line.
Vis sammendrag
Fibres Out of Line is an interactive art installation and performance for the 2021 Rhythm Perception and Production Workshop (RPPW). Visitors can watch the performance, and subsequently interact with the installation, all remotely via Zoom.
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Krzyzaniak, Michael Joseph; Veenstra, Frank; Erdem, Cagri; Glette, Kyrre & Jensenius, Alexander Refsum
(2020).
Interactive Rhythmic Robots.
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Krzyzaniak, Michael Joseph; Kwak, Dongho Daniel; Veenstra, Frank; Erdem, Cagri; Wallace, Benedikte & Jensenius, Alexander Refsum
(2020).
Dr. Squiggles rhythmical robots.
Vis sammendrag
Dr. Squiggles is an interactive musical robot that we designed, that plays rhythms by tapping. It listens for tapping produced by humans or other musical robots, and attempts to play along and improvise its own rhythms based on what it hears.
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(2020).
Evolutionary algorithms for intelligent robots.
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Fuhrer, Julian; Blenkmann, Alejandro Omar; Tørresen, Jim; Endestad, Tor & Glette, Kyrre
(2019).
Making Sense of Randomness?
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Glette, Kyrre; Nygaard, Tønnes Frostad & Vogt, Yngve
(2019).
Her er universitetets nest selvlærende robot.
[Fagblad].
Teknisk ukeblad.
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Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav
(2019).
Intelligent, Adaptive Robots in Real-World Scenarios.
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Tørresen, Jim; Glette, Kyrre & Ellefsen, Kai Olav
(2019).
Adaptive Robot Body and Control for Real-World Environments.
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Miseikis, Justinas; Brijacak, Inka; Yahyanejad, Saeed; Glette, Kyrre; Elle, Ole Jacob & Tørresen, Jim
(2019).
Two-Stage Transfer Learning for Heterogeneous Robot Detection and 3D Joint Position Estimation in a 2D Camera Image Using CNN.
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(2019).
Kunstig intelligens for tilpasningsdyktige roboter
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Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Lessons Learned from Real-World Experiments with
DyRET: the Dynamic Robot for Embodied Testing.
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Nygaard, Tønnes Frostad; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Self-Modifying Morphology Experiments with DyRET: Dynamic Robot for Embodied Testing.
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Nygaard, Tønnes Frostad; Nordmoen, Jørgen Halvorsen; Ellefsen, Kai Olav; Martin, Charles Patrick; Tørresen, Jim & Glette, Kyrre
(2019).
Experiences from Real-World Evolution with DyRET: Dynamic Robot for Embodied Testing.
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Nordmoen, Jørgen Halvorsen; Nygaard, Tønnes Frostad; Ellefsen, Kai Olav & Glette, Kyrre
(2019).
Evolved embodied phase coordination enables robust quadruped robot locomotion
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Ahmadi, Arash; Rosnes, Ida; Blicher, Pernille; Diekmann, Robin; Schuttpelz, Mark & Glette, Kyrre
[Vis alle 9 forfattere av denne artikkelen]
(2019).
Publisher Correction: Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V (Nature Communications, (2018), 9, 1, (5381), 10.1038/s41467-018-07797-4).
Nature Communications.
ISSN 2041-1723.
10(1).
doi:
10.1038/s41467-019-10070-x.
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Jensenius, Alexander Refsum; Martin, Charles Patrick; Erdem, Cagri; Lan, Qichao; Fuhrer, Julian Peter & Gonzalez Sanchez, Victor Evaristo
[Vis alle 8 forfattere av denne artikkelen]
(2019).
Self-playing Guitars.
Vis sammendrag
In this installation we explore how six self-playing guitars can entrain to each other. When they are left alone they will revert to playing a common pulse. As soon as they sense people in their surroundings they will start entraining to other pulses. The result is a fascinating exploration of a basic physical and cognitive concept, and the musically interesting patterns that emerge on the border between order and chaos.
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(2019).
Kunstig intelligens for tilpasningsdyktige roboter.
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Fuhrer, Julian Peter; Glette, Kyrre & Jensenius, Alexander Refsum
(2018).
Interactive Animation of the RITMO Logo.
Vis sammendrag
In this project the logo of RITMO is installed in an interactive animation. It is able to move in accordance with the frequency band of an audio input stream. That is to say, the RITMO logo interacts with the rhythmical streams of music.
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Fuhrer, Julian; Glette, Kyrre & Jensenius, Alexander Refsum
(2018).
Interactive Animation of RITMO Logo.
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Fuhrer, Julian; Glette, Kyrre & Jensenius, Alexander Refsum
(2018).
Interactive Opening Animation.
Vis sammendrag
With this project we installed the logo of RITMO in an interactive animation for the opening of the centre. The logo is enabled to receive audio input such that it is able to move in accordance with the frequency band of the input. That is to say, the logo is able to move along with rythmic streams of the music.
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(2018).
Robotics, Intelligent Systems and Evolutionary Computation
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(2018).
Automatic design of shapes and behaviors for robots.
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Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre
(2018).
Evolving a Repertoire of Controllers for a Multi-Function Swarm.
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Engebråten, Sondre Andreas; Yakimenko, Oleg; Moen, Hans Jonas Fossum & Glette, Kyrre
(2018).
Towards a Multi-Function Swarm That Adapts to User Preferences
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Nordmoen, Jørgen; Glette, Kyrre & Ellefsen, Kai Olav
(2021).
Enhancing MAP-Elites to overcome challenges in Evolutionary Robotics.
University of Oslo.
ISSN 1501-7710.
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Nygaard, Tønnes; Glette, Kyrre; Tørresen, Jim & Martin, Charles Patrick
(2020).
Legging It: An Evolutionary Approach to Morphological Adaptation for a Real-World Quadruped Robot.
Universitetet i Oslo.
ISSN 1501-7710.
Fulltekst i vitenarkiv
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