  We present a framework for learning visually-guided quadruped locomotion by
integrating exteroceptive sensing and central pattern generators (CPGs), i.e.
systems of coupled oscillators, into the deep reinforcement learning (DRL)
framework. Through both exteroceptive and proprioceptive sensing, the agent
learns to coordinate rhythmic behavior among different oscillators to track
velocity commands, while at the same time override these commands to avoid
collisions with the environment. We investigate several open robotics and
neuroscience questions: 1) What is the role of explicit interoscillator
couplings between oscillators, and can such coupling improve sim-to-real
transfer for navigation robustness? 2) What are the effects of using a
memory-enabled vs. a memory-free policy network with respect to robustness,
energy-efficiency, and tracking performance in sim-to-real navigation tasks? 3)
How do animals manage to tolerate high sensorimotor delays, yet still produce
smooth and robust gaits? To answer these questions, we train our perceptive
locomotion policies in simulation and perform sim-to-real transfers to the
Unitree Go1 quadruped, where we observe robust navigation in a variety of
scenarios. Our results show that the CPG, explicit interoscillator couplings,
and memory-enabled policy representations are all beneficial for energy
efficiency, robustness to noise and sensory delays of 90 ms, and tracking
performance for successful sim-to-real transfer for navigation tasks. Video
results can be found at https://youtu.be/wpsbSMzIwgM.
