Regenerating Soft Robots through Neural Cellular Automata
This work aims to improve the resilience and operational robustness of soft robots by enabling them to regenerate damaged components, which is a significant step for future physical soft robots.
This paper addresses the lack of regenerative capacity in machines by developing an approach for simulated soft robots to regrow damaged parts of their morphology. The method allows these robots to partially regenerate their original shape and regain some locomotion ability using only local cell information.
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. The code for the experiments in this paper is available at: \url{github.com/KazuyaHoribe/RegeneratingSoftRobots}.