# Advanced Skills by Learning Locomotion and Local Navigation End-to-End

arXiv: https://arxiv.org/abs/2209.12827 · HTML: https://mlsanity.com/paper/2209.12827/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 30 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Learning Quadrupedal Locomotion over Challenging Terrain](https://mlsanity.com/paper/2010.11251/) (2020)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Blind Bipedal Stair Traversal via Sim-to-Real Reinforcement Learning](https://mlsanity.com/paper/2105.08328/) (2021)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Control](https://mlsanity.com/paper/1909.06586/) (2019)
- [Fast and Efficient Locomotion via Learned Gait Transitions](https://mlsanity.com/paper/2104.04644/) (2021)
- [DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.08399/) (2019)
- [Learning a State Representation and Navigation in Cluttered and Dynamic Environments](https://mlsanity.com/paper/2103.04351/) (2021)
- [Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization](https://mlsanity.com/paper/2109.14549/) (2021)
- [Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds](https://mlsanity.com/paper/1712.02889/) (2017)
- [Imitation Learning from MPC for Quadrupedal Multi-Gait Control](https://mlsanity.com/paper/2103.14331/) (2021)
- [Cat-like Jumping and Landing of Legged Robots in Low-gravity Using Deep Reinforcement Learning](https://mlsanity.com/paper/2106.09357/) (2021)
- [Rolling in the Deep -- Hybrid Locomotion for Wheeled-Legged Robots using Online Trajectory Optimization](https://mlsanity.com/paper/1909.07193/) (2019)
- [Mini Cheetah, the Falling Cat: A Case Study in Machine Learning and Trajectory Optimization for Robot Acrobatics](https://mlsanity.com/paper/2109.04424/) (2021)
- [Fast Online Planning for Bipedal Locomotion via Centroidal Model Predictive Gait Synthesis](https://mlsanity.com/paper/2102.04122/) (2021)
- [Real-time Optimal Landing Control of the MIT Mini Cheetah](https://mlsanity.com/paper/2110.02799/) (2021)

## Built on by (most cited first, 30 of 52)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [Robot Parkour Learning](https://mlsanity.com/paper/2309.05665/) (2023)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [DTC: Deep Tracking Control](https://mlsanity.com/paper/2309.15462/) (2023)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids](https://mlsanity.com/paper/2502.20396/) (2025)
- [Barkour: Benchmarking Animal-level Agility with Quadruped Robots](https://mlsanity.com/paper/2305.14654/) (2023)
- [DribbleBot: Dynamic Legged Manipulation in the Wild](https://mlsanity.com/paper/2304.01159/) (2023)
- [Symmetry Considerations for Learning Task Symmetric Robot Policies](https://mlsanity.com/paper/2403.04359/) (2024)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Learning Visual Quadrupedal Loco-Manipulation from Demonstrations](https://mlsanity.com/paper/2403.20328/) (2024)
- [Learning coordinated badminton skills for legged manipulators](https://mlsanity.com/paper/2505.22974/) (2025)
- [RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards](https://mlsanity.com/paper/2407.11562/) (2024)
- [Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models](https://mlsanity.com/paper/2407.02666/) (2024)
- [Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Locomotion](https://mlsanity.com/paper/2212.14400/) (2022)
- [SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience](https://mlsanity.com/paper/2409.13678/) (2024)
- [Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation](https://mlsanity.com/paper/2410.03076/) (2024)
- [Spatially-Enhanced Recurrent Memory for Long-Range Mapless Navigation via End-to-End Reinforcement Learning](https://mlsanity.com/paper/2506.05997/) (2025)
- [Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning](https://mlsanity.com/paper/2403.03848/) (2024)
- [One Filter to Deploy Them All: Robust Safety for Quadrupedal Navigation in Unknown Environments](https://mlsanity.com/paper/2412.09989/) (2024)
- [Learning Diverse Skills for Local Navigation under Multi-constraint Optimality](https://mlsanity.com/paper/2310.02440/) (2023)
- [Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots](https://mlsanity.com/paper/2402.06143/) (2024)
- [Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion](https://mlsanity.com/paper/2405.19452/) (2024)
- [Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models](https://mlsanity.com/paper/2410.00231/) (2024)
- [Robust Robot Walker: Learning Agile Locomotion over Tiny Traps](https://mlsanity.com/paper/2409.07409/) (2024)
- [Constrained Skill Discovery: Quadruped Locomotion with Unsupervised Reinforcement Learning](https://mlsanity.com/paper/2410.07877/) (2024)
- [Unsupervised Skill Discovery as Exploration for Learning Agile Locomotion](https://mlsanity.com/paper/2508.08982/) (2025)
- [Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models](https://mlsanity.com/paper/2309.02976/) (2023)

## Abstract (arXiv)

The common approach for local navigation on challenging environments with legged robots requires path planning, path following and locomotion, which usually requires a locomotion control policy that accurately tracks a commanded velocity. However, by breaking down the navigation problem into these sub-tasks, we limit the robot's capabilities since the individual tasks do not consider the full solution space. In this work, we propose to solve the complete problem by training an end-to-end policy with deep reinforcement learning. Instead of continuously tracking a precomputed path, the robot needs to reach a target position within a provided time. The task's success is only evaluated at the end of an episode, meaning that the policy does not need to reach the target as fast as possible. It is free to select its path and the locomotion gait. Training a policy in this way opens up a larger set of possible solutions, which allows the robot to learn more complex behaviors. We compare our approach to velocity tracking and additionally show that the time dependence of the task reward is critical to successfully learn these new behaviors. Finally, we demonstrate the successful deployment of policies on a real quadrupedal robot. The robot is able to cross challenging terrains, which were not possible previously, while using a more energy-efficient gait and achieving a higher success rate.