Despite the recent successes in robotic locomotion control, the design of
robot relies heavily on human engineering. Automatic robot design has been a
long studied subject, but the recent progress has been slowed due to the large
combinatorial search space and the difficulty in evaluating the found
candidates. To address the two challenges, we formulate automatic robot design
as a graph search problem and perform evolution search in graph space. We
propose Neural Graph Evolution (NGE), which performs selection on current
candidates and evolves new ones iteratively. Different from previous
approaches, NGE uses graph neural networks to parameterize the control
policies, which reduces evaluation cost on new candidates with the help of
skill transfer from previously evaluated designs. In addition, NGE applies
Graph Mutation with Uncertainty (GM-UC) by incorporating model uncertainty,
which reduces the search space by balancing exploration and exploitation. We
show that NGE significantly outperforms previous methods by an order of
magnitude. As shown in experiments, NGE is the first algorithm that can
automatically discover kinematically preferred robotic graph structures, such
as a fish with two symmetrical flat side-fins and a tail, or a cheetah with
athletic front and back legs. Instead of using thousands of cores for weeks,
NGE efficiently solves searching problem within a day on a single 64 CPU-core
Amazon EC2 machine.

我们提出一种基于神经网络的图演化算法，在解决自动机器人设计中的组合问题和评估困难问题方面具有优越性能。与以往方法不同，我们使用图神经网络对控制策略进行参数化，同时运用包含模型不确定性的图突变技术，在探索与开发之间达到平衡，从而在单一机器上一天之内高效地解决搜索问题。