# GUIDE: Goal-Initialized Directional Understanding for End-to-End Legged Navigation

arXiv: https://arxiv.org/abs/2606.10832 · HTML: https://mlsanity.com/paper/2606.10832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 32 of its 41 references that are in the map.
- First posted on arXiv 9 Jun 2026.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations](https://mlsanity.com/paper/2008.05930/) (2020)
- [ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robots](https://mlsanity.com/paper/2306.14874/) (2023)
- [Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation](https://mlsanity.com/paper/1703.00420/) (2017)
- [Partially Observable Markov Decision Processes in Robotics: A Survey](https://mlsanity.com/paper/2209.10342/) (2022)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [ViPlanner: Visual Semantic Imperative Learning for Local Navigation](https://mlsanity.com/paper/2310.00982/) (2023)
- [From Perception to Decision: A Data-driven Approach to End-to-end Motion Planning for Autonomous Ground Robots](https://mlsanity.com/paper/1609.07910/) (2016)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response](https://mlsanity.com/paper/2312.11460/) (2023)
- [iPlanner: Imperative Path Planning](https://mlsanity.com/paper/2302.11434/) (2023)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation](https://mlsanity.com/paper/1609.06838/) (2016)
- [Learned Inertial Odometry for Autonomous Drone Racing](https://mlsanity.com/paper/2210.15287/) (2022)
- [Learned Perceptive Forward Dynamics Model for Safe and Platform-aware Robotic Navigation](https://mlsanity.com/paper/2504.19322/) (2025)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation](https://mlsanity.com/paper/1807.05424/) (2018)
- [Probabilistic Contact State Estimation for Legged Robots using Inertial Information](https://mlsanity.com/paper/2303.00538/) (2023)
- [SEA-Nav: Efficient Policy Learning for Safe and Agile Quadruped Navigation in Cluttered Environments](https://mlsanity.com/paper/2603.09460/) (2026)
- [DogLegs: Robust Proprioceptive State Estimation for Legged Robots Using Multiple Leg-Mounted IMUs](https://mlsanity.com/paper/2503.04580/) (2025)
- [AutoOdom: Learning Auto-regressive Proprioceptive Odometry for Legged Locomotion](https://mlsanity.com/paper/2511.18857/) (2025)
- [REASAN: Learning Reactive Safe Navigation for Legged Robots](https://mlsanity.com/paper/2512.09537/) (2025)

## Abstract (arXiv)

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.