  The physical design of a robot and the policy that controls its motion are
inherently coupled, and should be determined according to the task and
environment. In an increasing number of applications, data-driven and
learning-based approaches, such as deep reinforcement learning, have proven
effective at designing control policies. For most tasks, the only way to
evaluate a physical design with respect to such control policies is
empirical--i.e., by picking a design and training a control policy for it.
Since training these policies is time-consuming, it is computationally
infeasible to train separate policies for all possible designs as a means to
identify the best one. In this work, we address this limitation by introducing
a method that performs simultaneous joint optimization of the physical design
and control network. Our approach maintains a distribution over designs and
uses reinforcement learning to optimize a control policy to maximize expected
reward over the design distribution. We give the controller access to design
parameters to allow it to tailor its policy to each design in the distribution.
Throughout training, we shift the distribution towards higher-performing
designs, eventually converging to a design and control policy that are jointly
optimal. We evaluate our approach in the context of legged locomotion, and
demonstrate that it discovers novel designs and walking gaits, outperforming
baselines in both performance and efficiency.
