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, we seek to push the limits of end-to-end sim-to-real RL navigation by investigating whether a legged robot can internally maintain the spatial context required for long-horizon navigation. To this end, we study goal-initialized navigation, where the goal is provided only once at the beginning of an episode, with no subsequent external relative-goal updates. We present GUIDE, an end-to-end RL framework that jointly learns navigation and internal directional awareness from onboard observations. GUIDE leverages multi-frequency proprioceptive history to capture egomotion and auxiliary spatial-anchor prediction to maintain task-relevant spatial states, while temporal depth observations provide local environmental geometry. The navigation policy is trained entirely in simulation and transferred zero-shot to the real world. Across cluttered environments and structured mazes, GUIDE reliably avoids obstacles, escapes dead ends, and reaches distant goals using only onboard sensing. These results demonstrate that robust sim-to-real legged navigation can be achieved without continuously providing external robot-to-goal estimation, opening a promising direction for future research on more self-contained end-to-end navigation.