  This paper presents an online framework for synthesizing agile locomotion for
bipedal robots that adapts to unknown environments, modeling errors, and
external disturbances. To this end, we leverage step-to-step (S2S) dynamics
which has proven effective in realizing dynamic walking on underactuated robots
-- assuming known dynamics and environments. This paper considers the case of
uncertain models and environments and presents a data-driven representation of
the S2S dynamics that can be learned via an adaptive control approach that is
both data-efficient and easy to implement. The learned S2S controller generates
desired discrete foot placement, which is then realized on the full-order
dynamics of the bipedal robot by tracking desired outputs synthesized from the
given foot placement. The benefits of the proposed approach are twofold. First,
it improves the ability of the robot to walk at a given desired velocity when
compared to the non-adaptive baseline controller. Second, the data-driven
approach enables stable and agile locomotion under the effect of various
unknown disturbances: additional unmodeled payload, large robot model errors,
external disturbance forces, biased velocity estimation, and sloped terrains.
This is demonstrated through in-depth evaluation with a high-fidelity
simulation of the bipedal robot Cassie subject to the aforementioned
disturbances.
