  A necessary capability for humanoid robots is the ability to stand and walk
while rejecting natural disturbances. Recent progress has been made using
sim-to-real reinforcement learning (RL) to train such locomotion controllers,
with approaches differing mainly in their reward functions. However, prior
works lack a clear method to systematically test new reward functions and
compare controller performance through repeatable experiments. This limits our
understanding of the trade-offs between approaches and hinders progress. To
address this, we propose a low-cost, quantitative benchmarking method to
evaluate and compare the real-world performance of standing and walking (SaW)
controllers on metrics like command following, disturbance recovery, and energy
efficiency. We also revisit reward function design and construct a minimally
constraining reward function to train SaW controllers. We experimentally verify
that our benchmarking framework can identify areas for improvement, which can
be systematically addressed to enhance the policies. We also compare our new
controller to state-of-the-art controllers on the Digit humanoid robot. The
results provide clear quantitative trade-offs among the controllers and suggest
directions for future improvements to the reward functions and expansion of the
benchmarks.
