Morphological regeneration is an important feature that highlights the environ- mental 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 ad- dressing 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 inter- actions 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. The code for the experiments in this paper is available at: github.com/KazuyaHoribe/RegeneratingSoftRobots.
Regenerating Soft Robots through Neural Cellular Automata
2021-05-01
Kazuya Horibe 2021 , ' Regenerating Soft Robots through Neural Cellular Automata ' , EVOSTAR 2021 . https://doi.org/10.48550/arXiv.2102.02579
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Collective control of modular soft robots via embodied Spiking Neural Cellular Automata
BASE | 2022
|Constrained Optimization Using Cellular Automata
AIAA | 2004
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