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In this paper, we examine the problem of push recovery for bipedal robotlocomotion and present a reactive decision-making and robust planning frameworkfor locomotion resilient to external perturbations. Rejecting perturbations isan essential capability of bipedal robots and has been widely studied in thelocomotion literature. However, adversarial disturbances and aggressive turningcan lead to negative lateral step width (i.e., crossed-leg scenarios) withunstable motions and self-collision risks. These motion planning problems arecomputationally difficult and have not been explored under a hierarchicallyintegrated task and motion planning method. We explore a planning anddecision-making framework that closely ties linear-temporal-logic-basedreactive synthesis with trajectory optimization incorporating the robot'sfull-body dynamics, kinematics, and leg collision avoidance constraints.Between the high-level discrete symbolic decision-making and the low-levelcontinuous motion planning, behavior trees serve as a reactive interface tohandle perturbations occurring at any time of the locomotion process. Ourexperimental results show the efficacy of our method in generating resilientrecovery behaviors in response to diverse perturbations from any direction withbounded magnitudes.

Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery