Severe Damage Recovery in Evolving Soft Robots through Differentiable Programming
This addresses the challenge of damage recovery in soft robots, offering a novel approach that combines evolution and differentiable programming for improved robustness.
The paper tackles the problem of artificial robots lacking robustness to morphological damage by evolving neural cellular automata that can regenerate and regain over 80% of functionality after severe damage.
Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which locomoting robots are evolved and then given the ability to regenerate their morphology from damage through gradient-based training. Our approach thus combines the benefits of evolution to discover a wide range of different robot morphologies, with the efficiency of supervised training for robustness through differentiable update rules. The resulting neural cellular automata are able to grow virtual robots capable of regaining more than 80\% of their functionality, even after severe types of morphological damage.