  Robots have been used in all sorts of automation, and yet the design of
robots remains mainly a manual task. We seek to provide design tools to
automate the design of robots themselves. An important challenge in robot
design automation is the large and complex design search space which grows
exponentially with the number of components, making optimization difficult and
sample inefficient. In this work, we present Grammar-guided Latent Space
Optimization (GLSO), a framework that transforms design automation into a
low-dimensional continuous optimization problem by training a graph variational
autoencoder (VAE) to learn a mapping between the graph-structured design space
and a continuous latent space. This transformation allows optimization to be
conducted in a continuous latent space, where sample efficiency can be
significantly boosted by applying algorithms such as Bayesian Optimization.
GLSO guides training of the VAE using graph grammar rules and robot world space
features, such that the learned latent space focus on valid robots and is
easier for the optimization algorithm to explore. Importantly, the trained VAE
can be reused to search for designs specialized to multiple different tasks
without retraining. We evaluate GLSO by designing robots for a set of
locomotion tasks in simulation, and demonstrate that our method outperforms
related state-of-the-art robot design automation methods.
