mlsanityData to 14 Oct 2026

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

arXiv 2506.00472 ↗First posted 31 May 2025cs.ROcited by 3

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

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.

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.

Builds on

25 of 38 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024PA-LOCO: Learning Perturbation-Adaptive Locomotion for Quadruped Robots5 Jul
  2. 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers21 May
  3. 2024Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control30 Jan

Other references · 22

  1. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
  2. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  3. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  4. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  5. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  6. 2023Extreme Parkour with Legged Robotscited by 164
  7. 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
  8. 2020Dynamics Randomization Revisited:A Case Study for Quadrupedal Locomotioncited by 59
  9. 2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
  10. 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robotscited by 48
  11. 2022Perceptive Locomotion through Nonlinear Model Predictive Controlcited by 38
  12. 2022CPG-RL: Learning Central Pattern Generators for Quadruped Locomotioncited by 34
10 more
  1. 2022Learning Torque Control for Quadrupedal Locomotioncited by 29
  2. 2023Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfercited by 22
  3. 2023Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robotscited by 20
  4. 2024Learning H-Infinity Locomotion Controlcited by 11
  5. 2023Learning Agile Locomotion and Adaptive Behaviors via RL-augmented MPCcited by 10
  6. 2024Robust Ladder Climbing with a Quadrupedal Robotcited by 10
  7. 2023Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terraincited by 7
  8. 2022Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balancecited by 6
  9. 2023End-to-End Reinforcement Learning for Torque Based Variable Height Hoppingcited by 3
  10. 2024GRAM: Generalization in Deep RL with a Robust Adaptation Modulecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Controlcited by 0
  2. 2025Cross-Platform Learnable Fuzzy Gain-Scheduled Proportional-Integral-Derivative Controller Tuning via Physics-Constrained Meta-Learning and Reinforcement Learning Adaptationcited by 0
  3. 2026Efficient Test-Time Adaptation For Robot Policiescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
  2. 2026Force-Aware Reinforcement Learning with Hybrid Sensorless Force Estimation for Wheeled-Legged Loco-Manipulation
  3. 2026GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control
  4. 2025Whole-Body Constrained Learning for Legged Locomotion via Hierarchical Optimization
  5. 2025Learning a Unified Policy for Position and Force Control in Legged Loco-Manipulation
  6. 2025Constrained Reinforcement Learning for Unstable Point-Feet Bipedal Locomotion Applied to the Bolt Robot
  7. 2025Variable Stiffness for Robust Locomotion through Reinforcement Learning
  8. 2026ReCo: Response-Consistent Locomotion with Policy-Aware MPC for Legged Manipulation ↗