  This paper addresses the challenge of terrain-adaptive dynamic locomotion in
humanoid robots, a problem traditionally tackled by optimization-based methods
or reinforcement learning (RL). Optimization-based methods, such as
model-predictive control, excel in finding optimal reaction forces and
achieving agile locomotion, especially in quadruped, but struggle with the
nonlinear hybrid dynamics of legged systems and the real-time computation of
step location, timing, and reaction forces. Conversely, RL-based methods show
promise in navigating dynamic and rough terrains but are limited by their
extensive data requirements. We introduce a novel locomotion architecture that
integrates a neural network policy, trained through RL in simplified
environments, with a state-of-the-art motion controller combining
model-predictive control (MPC) and whole-body impulse control (WBIC). The
policy efficiently learns high-level locomotion strategies, such as gait
selection and step positioning, without the need for full dynamics simulations.
This control architecture enables humanoid robots to dynamically navigate
discrete terrains, making strategic locomotion decisions (e.g., walking,
jumping, and leaping) based on ground height maps. Our results demonstrate that
this integrated control architecture achieves dynamic locomotion with
significantly fewer training samples than conventional RL-based methods and can
be transferred to different humanoid platforms without additional training. The
control architecture has been extensively tested in dynamic simulations,
accomplishing terrain height-based dynamic locomotion for three different
robots.
