# Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1901.07517 · HTML: https://mlsanity.com/paper/1901.07517/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2019.

## Built on by (most cited first, 30 of 32)

- [Language to Rewards for Robotic Skill Synthesis](https://mlsanity.com/paper/2306.08647/) (2023)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions](https://mlsanity.com/paper/2203.15103/) (2022)
- [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://mlsanity.com/paper/2202.05481/) (2022)
- [A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning](https://mlsanity.com/paper/2208.07860/) (2022)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Learning Humanoid Standing-up Control across Diverse Postures](https://mlsanity.com/paper/2502.08378/) (2025)
- [DribbleBot: Dynamic Legged Manipulation in the Wild](https://mlsanity.com/paper/2304.01159/) (2023)
- [Learning and Adapting Agile Locomotion Skills by Transferring Experience](https://mlsanity.com/paper/2304.09834/) (2023)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [Learning and Deploying Robust Locomotion Policies with Minimal Dynamics Randomization](https://mlsanity.com/paper/2209.12878/) (2022)
- [Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild](https://mlsanity.com/paper/2304.10888/) (2023)
- [Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization](https://mlsanity.com/paper/2207.02016/) (2022)
- [Cell-Free Latent Go-Explore](https://mlsanity.com/paper/2208.14928/) (2022)
- [Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations](https://mlsanity.com/paper/2203.02857/) (2022)
- [Roll-Drop: accounting for observation noise with a single parameter](https://mlsanity.com/paper/2304.13150/) (2023)
- [Learning Setup Policies: Reliable Transition Between Locomotion Behaviours](https://mlsanity.com/paper/2101.09391/) (2021)
- [Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning](https://mlsanity.com/paper/2107.02955/) (2021)
- [Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration](https://mlsanity.com/paper/2206.09627/) (2022)
- [CCDP: Composition of Conditional Diffusion Policies with Guided Sampling](https://mlsanity.com/paper/2503.15386/) (2025)
- [Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots](https://mlsanity.com/paper/2506.05516/) (2025)
- [Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery](https://mlsanity.com/paper/2510.22336/) (2025)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [Direct Random Search for Fine Tuning of Deep Reinforcement Learning Policies](https://mlsanity.com/paper/2109.05604/) (2021)
- [Know your limits! Optimize the robot's behavior through self-awareness](https://mlsanity.com/paper/2409.10308/) (2024)
- [Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning](https://mlsanity.com/paper/2505.06561/) (2025)
- [TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards](https://mlsanity.com/paper/2602.05596/) (2026)
- [LatentMimic: Terrain-Adaptive Locomotion via Latent Space Imitation](https://mlsanity.com/paper/2604.16440/) (2026)
- [Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning](https://mlsanity.com/paper/2606.14270/) (2026)

## Abstract (arXiv)

The ability to recover from a fall is an essential feature for a legged robot to navigate in challenging environments robustly. Until today, there has been very little progress on this topic. Current solutions mostly build upon (heuristically) predefined trajectories, resulting in unnatural behaviors and requiring considerable effort in engineering system-specific components. In this paper, we present an approach based on model-free Deep Reinforcement Learning (RL) to control recovery maneuvers of quadrupedal robots using a hierarchical behavior-based controller. The controller consists of four neural network policies including three behaviors and one behavior selector to coordinate them. Each of them is trained individually in simulation and deployed directly on a real system. We experimentally validate our approach on the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12 degrees of freedom. With our method, ANYmal manifests dynamic and reactive recovery behaviors to recover from an arbitrary fall configuration within less than 5 seconds. We tested the recovery maneuver more than 100 times, and the success rate was higher than 97 %.