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This study proposes an integrated task and motion planning method for dynamiclocomotion in partially observable environments with multi-level safetyguarantees. This layered planning framework is composed of a high-levelsymbolic task planner and a low-level phase-space motion planner. A beliefabstraction at the task planning level enables belief estimation of dynamicobstacle locations and guarantees navigation safety with collision avoidance.The high-level task planner, i.e., a two-level navigation planner, employslinear temporal logic for a reactive game synthesis between the robot and itsenvironment while incorporating low-level safe keyframe policies into formaltask specification design. The synthesized task planner commands a series oflocomotion actions including walking step length, step height, and headingangle changes, to the underlying keyframe decision-maker, which furtherdetermines the robot center-of-mass apex velocity keyframe. The low-levelphase-space planner uses a reduced-order locomotion model to generatenon-periodic trajectories meeting balancing safety criteria for straight andsteering walking. These criteria are characterized by constraints on locomotionkeyframe states, and are used to define keyframe transition policies viaviability kernels. Simulation results of a Cassie bipedal robot designed byAgility Robotics demonstrate locomotion maneuvering in a three-dimensional,partially observable environment consisting of dynamic obstacles and uneventerrain.

Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain