Prepare to Turn: Learning Anticipatory Maneuvering for Fast Sequential Quadrupedal Navigation
Zunzhi You, Yunke Wang, Haolan Guo, Chang Xu
Fast quadrupedal navigation requires preparing for the next turn while approaching the current goal. Policies trained to reach and remain at a single target can instead produce stop-and-go motion when applied to sequential goals, compromising its agility potential. We introduce PrepTurn, a reinforcement learning framework for anticipatory maneuvering in sequential local navigation. PrepTurn maps the current and next pose goals directly to joint actions, using the upcoming goal to inform how the robot should approaches the current one. Its persistent-credit reward encourages timely completion of the whole sequence by continuing to reward reached goals while moving on and preparing subsequent maneuvers, and an automatic curriculum that gradually increases goal difficulty is critical for learning stable turning behaviors. Together, these components enable the policy to coordinate approach speed and reorientation across goal transitions without prescribed trajectories or arrival times. Simulation and onboard Unitree Go2 experiments demonstrate fast, efficient sequential traversal with high completion and reduced stop-and-go motion. We further show that when the goal-reaching tolerances are more relaxed, the policy learns to exploit the freedom allowed by the task with emergent behaviors, such as turning to the next goal in advance and cutting corners within the permitted regions behaviors, to traverse goal sequences more efficiently.
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