Robotics, Evolution and Art Laboratory

The vision of the lab is to do research on the border between fiction and fact, to create new, feasible scenarios for the society of tomorrow. The lab has an interdisciplinary experimental practice. The lab will conceptually explore what-if scenarios with artistic projects as well as conduct fundamental science and engineering to work towards realization of these scenarios. Academically, the group is rooted in the arts, robotics, artificial life and evolutionary computation, but generally the research agenda is driven by opportunities arising both within the science and art & design communities.

Goals

  • Through art explore new potentialities emerging from the development of science and technology
  • Develop commercially viable products and solutions
  • Develop human-in-the-loop design methodologies based on artificial evolution and artificial intelligence
  • Develop technology that enables new applications of robotics typically with a focus on empowerment and accessibility

News & Talks

News Twitter

#ALIFE2021 Calls for contributions are now open!
⚡️Call for Workshops, Special Sessions & Tutorials (deadline: 31 Jan, 2021)
⚡️Call for Papers & Extended Abstracts (deadline: 07 Mar, 2021)
⚡️Call for art & vizualizations (deadline: 25 Apr, 2021)
https://www.robot100.cz/alife2021

A collection of artificial life environments to study open-endedness. @kenneth0stanley @err_more @leespector @RELenski @CharlesOfria @ThomasMiconi @IMordatch Any major ones we forgot? Guess which framework will be next 🤓

Hey, everyone!

Remember the sudoku web app we released a couple of months ago? We are doing a second experiment for our paper, but using a dungeon-like game this time.

If you have Python, Java, and 20 minutes to spare, play this game and help us!

https://github.com/miguelgondu/adaptive_zelda

I'm super pleased to release EvoStrat https://github.com/rasmusbergpalm/evostrat

It's a library that makes Evolutionary Strategies (ES) simple to use.

I'm really happy how cleanly it separates the environment, the reinforcement learning agent, the population distribution and the optimization.

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