  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}.
