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GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation
End-to-end reinforcement learning (RL) has shown strong potential for legged robot navigation, yet existing approaches commonly rely on continuously updated robot-to-goal states from external state estimation modules, leaving part of the navigation problem outside the learned policy. In this work, w
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