# FRASA: An End-to-End Reinforcement Learning Agent for Fall Recovery and Stand Up of Humanoid Robots

arXiv: https://arxiv.org/abs/2410.08655 · HTML: https://mlsanity.com/paper/2410.08655/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## 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, 11 of 11)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity](https://mlsanity.com/paper/1902.05605/) (2019)
- [Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking](https://mlsanity.com/paper/2404.19173/) (2024)
- [MPC for Humanoid Gait Generation: Stability and Feasibility](https://mlsanity.com/paper/1901.08505/) (2019)
- [Rhoban Football Club: RoboCup Humanoid Kid-Size 2023 Champion Team Paper](https://mlsanity.com/paper/2402.00612/) (2024)
- [Push Recovery of a Humanoid Robot Based on Model Predictive Control and Capture Point](https://mlsanity.com/paper/1612.08034/) (2016)
- [Capture Steps: Robust Walking for Humanoid Robots](https://mlsanity.com/paper/2011.02793/) (2020)

## Built on by (most cited first, 10 of 10)

- [Learning Humanoid Standing-up Control across Diverse Postures](https://mlsanity.com/paper/2502.08378/) (2025)
- [Learning Getting-Up Policies for Real-World Humanoid Robots](https://mlsanity.com/paper/2502.12152/) (2025)
- [Towards Safe Maneuvering of Double-Ackermann-Steering Robots with a Soft Actor-Critic Framework](https://mlsanity.com/paper/2510.10332/) (2025)
- [Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery](https://mlsanity.com/paper/2510.22336/) (2025)
- [Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery](https://mlsanity.com/paper/2603.08619/) (2026)
- [Learning to Get Up Across Morphologies: Zero-Shot Recovery with a Unified Humanoid Policy](https://mlsanity.com/paper/2512.12230/) (2025)
- [ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions](https://mlsanity.com/paper/2602.14726/) (2026)
- [Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors](https://mlsanity.com/paper/2605.18611/) (2026)
- [Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids](https://mlsanity.com/paper/2606.12814/) (2026)
- [Robust Fall Recovery for Armless Bipedal-Wheeled Robots Via Force-Guided Learning](https://mlsanity.com/paper/2606.14270/) (2026)

## Abstract (arXiv)

Humanoid robotics faces significant challenges in achieving stable locomotion and recovering from falls in dynamic environments. Traditional methods, such as Model Predictive Control (MPC) and Key Frame Based (KFB) routines, either require extensive fine-tuning or lack real-time adaptability. This paper introduces FRASA, a Deep Reinforcement Learning (DRL) agent that integrates fall recovery and stand up strategies into a unified framework. Leveraging the Cross-Q algorithm, FRASA significantly reduces training time and offers a versatile recovery strategy that adapts to unpredictable disturbances. Comparative tests on Sigmaban humanoid robots demonstrate FRASA superior performance against the KFB method deployed in the RoboCup 2023 by the Rhoban Team, world champion of the KidSize League.