This paper presents a framework for synthesizing bipedal robotic walking that
adapts to unknown environment and dynamics error via a data-driven step-to-step
(S2S) dynamics model. We begin by synthesizing an S2S controller that
stabilizes the walking using foot placement through nominal S2S dynamics from
the hybrid linear inverted pendulum (H-LIP) model. Next, a data-driven
representation of the S2S dynamics of the robot is learned online via classical
adaptive control methods. The desired discrete foot placement on the robot is
thereby realized by proper continuous output synthesis capturing the
data-driven S2S controller coupled with a low-level tracking controller. The
proposed approach is implemented in simulation on an underactuated 3D bipedal
robot, Cassie, and improved reference velocity tracking is demonstrated. The
proposed approach is also able to realize walking behavior that is robustly
adaptive to unknown loads, inaccurate robot models, external disturbance
forces, biased velocity estimation, and unknown slopes