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This study proposes a hierarchically integrated framework for safe task andmotion planning (TAMP) of bipedal locomotion in a partially observableenvironment with dynamic obstacles and uneven terrain. The high-level taskplanner employs linear temporal logic (LTL) for a reactive game synthesisbetween the robot and its environment and provides a formal guarantee onnavigation safety and task completion. To address environmental partialobservability, a belief abstraction is employed at the high-level navigationplanner to estimate the dynamic obstacles' location. Accordingly, a synthesizedaction planner sends a set of locomotion actions to the middle-level motionplanner, while incorporating safe locomotion specifications extracted fromsafety theorems based on a reduced-order model (ROM) of the locomotion process.The motion planner employs the ROM to design safety criteria and a samplingalgorithm to generate non-periodic motion plans that accurately trackhigh-level actions. At the low level, a foot placement controller based on anangular-momentum linear inverted pendulum model is implemented and integratedwith an ankle-actuated passivity-based controller for full-body trajectorytracking. To address external perturbations, this study also investigates safesequential composition of the keyframe locomotion state and achieves robusttransitions against external perturbations through reachability analysis. Theoverall TAMP framework is validated with extensive simulations and hardwareexperiments on bipedal walking robots Cassie and Digit designed by AgilityRobotics.

Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments