# Learning Arm-Assisted Fall Damage Reduction and Recovery for Legged Mobile Manipulators

arXiv: https://arxiv.org/abs/2303.05486 · HTML: https://mlsanity.com/paper/2303.05486/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2023.

## Built on by (most cited first, 22 of 22)

- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [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)
- [Learning Humanoid Standing-up Control across Diverse Postures](https://mlsanity.com/paper/2502.08378/) (2025)
- [RAYEN: Imposition of Hard Convex Constraints on Neural Networks](https://mlsanity.com/paper/2307.08336/) (2023)
- [Learning Getting-Up Policies for Real-World Humanoid Robots](https://mlsanity.com/paper/2502.12152/) (2025)
- [Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-End](https://mlsanity.com/paper/2310.03581/) (2023)
- [Learning Force Control for Legged Manipulation](https://mlsanity.com/paper/2405.01402/) (2024)
- [Learning coordinated badminton skills for legged manipulators](https://mlsanity.com/paper/2505.22974/) (2025)
- [Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation](https://mlsanity.com/paper/2410.13817/) (2024)
- [Learning Whole-Body Loco-Manipulation for Omni-Directional Task Space Pose Tracking with a Wheeled-Quadrupedal-Manipulator](https://mlsanity.com/paper/2412.03012/) (2024)
- [Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots](https://mlsanity.com/paper/2402.06143/) (2024)
- [SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training](https://mlsanity.com/paper/2506.04147/) (2025)
- [Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains](https://mlsanity.com/paper/2402.06783/) (2024)
- [Dynamic object goal pushing with mobile manipulators through model-free constrained reinforcement learning](https://mlsanity.com/paper/2502.01546/) (2025)
- [Robot Crash Course: Learning Soft and Stylized Falling](https://mlsanity.com/paper/2511.10635/) (2025)
- [Learning to Recover: Dynamic Reward Shaping with Wheel-Leg Coordination for Fallen Robots](https://mlsanity.com/paper/2506.05516/) (2025)
- [Learning Rapid Turning, Aerial Reorientation, and Balancing using Manipulator as a Tail](https://mlsanity.com/paper/2407.10420/) (2024)
- [QuadWBG: Generalizable Quadrupedal Whole-Body Grasping](https://mlsanity.com/paper/2411.06782/) (2024)
- [CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection](https://mlsanity.com/paper/2604.07457/) (2026)
- [Bicycle Acrobatics with Reinforcement Learning](https://mlsanity.com/paper/2608.00880/) (2026)

## Abstract (arXiv)

Adaptive falling and recovery skills greatly extend the applicability of robot deployments. In the case of legged mobile manipulators, the robot arm could adaptively stop the fall and assist the recovery. Prior works on falling and recovery strategies for legged mobile manipulators usually rely on assumptions such as inelastic collisions and falling in defined directions to enable real-time computation. This paper presents a learning-based approach to reducing fall damage and recovery. An asymmetric actor-critic training structure is used to train a time-invariant policy with time-varying reward functions. In simulated experiments, the policy recovers from 98.9% of initial falling configurations. It reduces base contact impulse, peak joint internal forces, and base acceleration during the fall compared to the baseline methods. The trained control policy is deployed and extensively tested on the ALMA robot hardware. A video summarizing the proposed method and the hardware tests is available at https://youtu.be/avwg2HqGi8s.