- author = "Kazuya Horibe and Kathryn Walker and Sebastian Risi",
- title = "Regenerating Soft Robots through Neural Cellular Automata",
- booktitle = "EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming",
- year = "2021",
- editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
- series = "LNCS",
- volume = "12691",
- publisher = "Springer Verlag",
- address = "Virtual Event",
- pages = "36--50",
- month = "7-9 " # apr,
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Regeneration, soft robots, neural cellular automata, NCA, CA, ANN, damage recovering, evosoro",
- isbn13 = "978-3-030-72811-3",
-
URL = "
https://arxiv.org/abs/2102.02579",
-
DOI = "
10.1007/978-3-030-72812-0_3",
-
code_url = "http://github.com/KazuyaHoribe/RegeneratingSoftRobots",
- size = "12 pages",
- abstract = "Morphological regeneration is an important feature that highlights the environmental adaptive capacity of biological systems. Lack of this regenerative capacity significantly limits the resilience of machines and the environments they can operate in. To aid in addressing this gap, we develop an approach for simulated soft robots to regrow parts of their morphology when being damaged. Although numerical simulations using soft robots have played an important role in their design, evolving soft robots with regenerative capabilities have so far received comparable little attention. Here we propose a model for soft robots that regenerate through a neural cellular automata. Importantly, this approach only relies on local cell information to regrow damaged components, opening interesting possibilities for physical regenerable soft robots in the future. Our approach allows simulated soft robots that are damaged to partially regenerate their original morphology through local cell interactions alone and regain some of their ability to locomote. These results take a step towards equipping artificial systems with regenerative capacities and could potentially allow for more robust operations in a variety of situations and environments.",
-
notes = "Sea slug regenerate body from head in 9 days vox???
simulation engine. feedforward ANN and Recurrent ANN
LSTM. fitness = distance traveled by centre of mass.
Evolve growth rule for 2D and 3D CA. Eg biped and
S-type, L-type, zigzag. pop=100, 300 gens.
One fixed cut (damage) applied.
Such,2017,arXiv. Locomotion limited recovery of locomotion after regrowth after damage.
Like Hopfield (ANN) networks.
http://www.evostar.org/2021/eurogp/ Part of \cite{Hu:2021:GP} EuroGP'2021 held in conjunction with EvoCOP2021, EvoMusArt2021 and EvoApplications2021",
