  In this paper, we examine the problem of push recovery for bipedal robot
locomotion and present a reactive decision-making and robust planning framework
for locomotion resilient to external perturbations. Rejecting perturbations is
an essential capability of bipedal robots and has been widely studied in the
locomotion literature. However, adversarial disturbances and aggressive turning
can lead to negative lateral step width (i.e., crossed-leg scenarios) with
unstable motions and self-collision risks. These motion planning problems are
computationally difficult and have not been explored under a hierarchically
integrated task and motion planning method. We explore a planning and
decision-making framework that closely ties linear-temporal-logic-based
reactive synthesis with trajectory optimization incorporating the robot's
full-body dynamics, kinematics, and leg collision avoidance constraints.
Between the high-level discrete symbolic decision-making and the low-level
continuous motion planning, behavior trees serve as a reactive interface to
handle perturbations occurring at any time of the locomotion process. Our
experimental results show the efficacy of our method in generating resilient
recovery behaviors in response to diverse perturbations from any direction with
bounded magnitudes.
