  This work explores an innovative algorithm designed to enhance the mobility
of underactuated bipedal robots across challenging terrains, especially when
navigating through spaces with constrained opportunities for foot support, like
steps or stairs. By combining ankle torque with a refined angular
momentum-based linear inverted pendulum model (ALIP), our method allows
variability in the robot's center of mass height. We employ a dual-strategy
controller that merges virtual constraints for precise motion regulation across
essential degrees of freedom with an ALIP-centric model predictive control
(MPC) framework, aimed at enforcing gait stability. The effectiveness of our
feedback design is demonstrated through its application on the Cassie bipedal
robot, which features 20 degrees of freedom. Key to our implementation is the
development of tailored nominal trajectories and an optimized MPC that reduces
the execution time to under 500 microseconds--and, hence, is compatible with
Cassie's controller update frequency. This paper not only showcases the
successful hardware deployment but also demonstrates a new capability, a
bipedal robot using a moving walkway.
