  This work presents a deep reinforcement learning-based approach to develop a
policy for robot-agnostic locomotion control. Our method involves training an
agent equipped with memory, implemented as a recurrent policy, on a diverse set
of procedurally generated quadruped robots. We demonstrate that the policies
trained by our framework transfer seamlessly to both simulated and real-world
quadrupeds not encountered during training, maintaining high-quality motion
across platforms. Through a series of simulation and hardware experiments, we
highlight the critical role of the recurrent unit in enabling generalization,
rapid adaptation to changes in the robot's dynamic properties, and sample
efficiency.
