# Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots

arXiv: https://arxiv.org/abs/2506.00472 · HTML: https://mlsanity.com/paper/2506.00472/

## Key facts

- Cited by 3 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 25 of its 38 references that are in the map.
- First posted on arXiv 31 May 2025.

## 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, 25 of 25)

- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning](https://mlsanity.com/paper/2109.11978/) (2021)
- [RMA: Rapid Motor Adaptation for Legged Robots](https://mlsanity.com/paper/2107.04034/) (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)
- [Extreme Parkour with Legged Robots](https://mlsanity.com/paper/2309.14341/) (2023)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning](https://mlsanity.com/paper/2301.10602/) (2023)
- [Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotion](https://mlsanity.com/paper/2011.02404/) (2020)
- [RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Control](https://mlsanity.com/paper/2012.03094/) (2020)
- [Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots](https://mlsanity.com/paper/2405.01792/) (2024)
- [Perceptive Locomotion through Nonlinear Model Predictive Control](https://mlsanity.com/paper/2208.08373/) (2022)
- [CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion](https://mlsanity.com/paper/2211.00458/) (2022)
- [Learning Torque Control for Quadrupedal Locomotion](https://mlsanity.com/paper/2203.05194/) (2022)
- [Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer](https://mlsanity.com/paper/2304.09434/) (2023)
- [Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots](https://mlsanity.com/paper/2307.10079/) (2023)
- [Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers](https://mlsanity.com/paper/2405.12424/) (2024)
- [Learning H-Infinity Locomotion Control](https://mlsanity.com/paper/2404.14405/) (2024)
- [Learning Agile Locomotion and Adaptive Behaviors via RL-augmented MPC](https://mlsanity.com/paper/2310.09442/) (2023)
- [Robust Ladder Climbing with a Quadrupedal Robot](https://mlsanity.com/paper/2409.17731/) (2024)
- [PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots](https://mlsanity.com/paper/2407.04224/) (2024)
- [Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain](https://mlsanity.com/paper/2307.04030/) (2023)
- [Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balance](https://mlsanity.com/paper/2201.11928/) (2022)
- [End-to-End Reinforcement Learning for Torque Based Variable Height Hopping](https://mlsanity.com/paper/2307.16676/) (2023)
- [GRAM: Generalization in Deep RL with a Robust Adaptation Module](https://mlsanity.com/paper/2412.04323/) (2024)

## Built on by (most cited first, 3 of 3)

- [Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control](https://mlsanity.com/paper/2509.13737/) (2025)
- [Cross-Platform Learnable Fuzzy Gain-Scheduled Proportional-Integral-Derivative Controller Tuning via Physics-Constrained Meta-Learning and Reinforcement Learning Adaptation](https://mlsanity.com/paper/2511.06500/) (2025)
- [Efficient Test-Time Adaptation For Robot Policies](https://mlsanity.com/paper/2610.20318/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL)-based methods have significantly improved the locomotion performance of legged robots. However, these motion policies face significant challenges when deployed in the real world. Robots operating in uncertain environments struggle to adapt to payload variations and external disturbances, resulting in severe degradation of motion performance. In this work, we propose a novel Hybrid Force-Position Locomotion Policy (HFPLP) learning framework, where the action space of the policy is defined as a combination of target joint positions and feedforward torques, enabling the robot to rapidly respond to payload variations and external disturbances. In addition, the proposed Disturbance-Aware Adaptive Compensation (DAAC) provides compensation actions in the torque space based on external disturbance estimation, enhancing the robot's adaptability to dynamic environmental changes. We validate our approach in both simulation and real-world deployment, demonstrating that it outperforms existing methods in carrying payloads and resisting disturbances.