mlsanityData to 14 Oct 2026

Dynamic Adaptive Legged Locomotion Policy via Decoupling Reaction Force Control and Gait Control

arXiv 2509.13737 ↗First posted 17 Sep 2025cs.ROcited by 0

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

Abstract (arXiv)

While Reinforcement Learning (RL) has achieved remarkable progress in legged locomotion control, it often suffers from performance degradation in out-of-distribution (OOD) conditions and discrepancies between the simulation and the real environments. Instead of mainly relying on domain randomization (DR) to best cover the real environments and thereby close the sim-to-real gap and enhance robustness, this work proposes an emerging decoupled framework that acquires fast online adaptation ability and mitigates the sim-to-real problems in unfamiliar environments by isolating stance-leg control and swing-leg control. Various simulation and real-world experiments demonstrate its effectiveness against horizontal force disturbances, uneven terrains, heavy and biased payloads, and sim-to-real gap.

Key facts. No citing papers in the map yet (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 17 Sep 2025.

Builds on

25 of 38 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Integrating Trajectory Optimization and Reinforcement Learning for Quadrupedal Jumping with Terrain-Adaptive Landing16 Sep
  2. 2025Disturbance-Aware Adaptive Compensation in Hybrid Force-Position Locomotion Policy for Legged Robots31 May

Other references · 23

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  3. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  4. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  5. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 508
  6. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  7. 2018Sim-to-Real: Learning Agile Locomotion For Quadruped Robotscited by 319
  8. 2022Rapid Locomotion via Reinforcement Learningcited by 168
  9. 2022Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behaviorcited by 138
  10. 2023DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learningcited by 66
  11. 2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
  12. 2023DTC: Deep Tracking Controlcited by 56
11 more
  1. 2019Highly Dynamic Quadruped Locomotion via Whole-Body Impulse Control and Model Predictive Controlcited by 54
  2. 2023Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Modelscited by 37
  3. 2023RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotioncited by 26
  4. 2024Learning-based legged locomotion; state of the art and future perspectivescited by 23
  5. 2025High-speed control and navigation for quadrupedal robots on complex and discrete terraincited by 19
  6. 2023Learning Agile Locomotion and Adaptive Behaviors via RL-augmented MPCcited by 10
  7. 2022Quadruped Capturability and Push Recovery via a Switched-Systems Characterization of Dynamic Balancecited by 6
  8. 2022Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Modelscited by 5
  9. 2024Chance-Constrained Convex MPC for Robust Quadruped Locomotion Under Parametric and Additive Uncertaintiescited by 2
  10. 2025Learning Robotic Policy with Imagined Transition: Mitigating the Trade-off between Robustness and Optimalitycited by 2
  11. 2025A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotioncited by 1

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  1. 2025Variable Stiffness for Robust Locomotion through Reinforcement Learning
  2. 2025FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
  3. 2020Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion
  4. 2024Learning to enhance multi-legged robot on rugged landscapes
  5. 2023Learning Robust, Agile, Natural Legged Locomotion Skills in the Wild
  6. 2026GPO: Growing Policy Optimization for Legged Robot Locomotion and Whole-Body Control
  7. 2025Whole-Body Constrained Learning for Legged Locomotion via Hierarchical Optimization
  8. 2024Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains