  We present ZSL-RPPO, an improved zero-shot learning architecture that
overcomes the limitations of teacher-student neural networks and enables
generating robust, reliable, and versatile locomotion for quadrupedal robots in
challenging terrains. We propose a new algorithm RPPO (Recurrent Proximal
Policy Optimization) that directly trains recurrent neural network in partially
observable environments and results in more robust training using domain
randomization. Our locomotion controller supports extensive perturbation across
simulation-to-reality transfer for both intrinsic and extrinsic physical
parameters without further fine-tuning. This can avoid the significant decline
of student's performance during simulation-to-reality transfer and therefore
enhance the robustness and generalization of the locomotion controller. We
deployed our controller on the Unitree A1 and Aliengo robots in real
environment and exteroceptive perception is provided by either a solid-state
Lidar or a depth camera. Our locomotion controller was tested in various
challenging terrains like slippery surfaces, Grassy Terrain, and stairs. Our
experiment results and comparison show that our approach significantly
outperforms the state-of-the-art.
