mlsanityData to 14 Oct 2026

Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations

arXiv 2510.13488 ↗First posted 15 Oct 2025cs.ROcited by 1

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

Abstract (arXiv)

Legged robots, particularly quadrupeds, excel at navigating rough terrains, yet their performance under vertical ground perturbations, such as those from oscillating surfaces, remains underexplored. This study introduces a novel approach to enhance quadruped locomotion robustness by training the Unitree Go2 robot on an oscillating bridge - a 13.24-meter steel-and-concrete structure with a 2.0 Hz eigenfrequency designed to perturb locomotion. Using Reinforcement Learning (RL) with the Proximal Policy Optimization (PPO) algorithm in a MuJoCo simulation, we trained 15 distinct locomotion policies, combining five gaits (trot, pace, bound, free, default) with three training conditions: rigid bridge and two oscillating bridge setups with differing height regulation strategies (relative to bridge surface or ground). Domain randomization ensured zero-shot transfer to the real-world bridge. Our results demonstrate that policies trained on the oscillating bridge exhibit superior stability and adaptability compared to those trained on rigid surfaces. Our framework enables robust gait patterns even without prior bridge exposure. These findings highlight the potential of simulation-based RL to improve quadruped locomotion during dynamic ground perturbations, offering insights for designing robots capable of traversing vibrating environments.

Key facts. Cited by 1 paper 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 19 of its 37 references that are in the map. First posted on arXiv 15 Oct 2025.

Builds on

19 of 37 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion11 Mar
  2. 2024SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience20 Sep
  3. 2024One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion10 Sep
  4. 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers21 May

Other references · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2021Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learningcited by 512
  4. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  5. 2022Rapid Locomotion via Reinforcement Learningcited by 168
  6. 2023Extreme Parkour with Legged Robotscited by 164
  7. 2023ANYmal Parkour: Learning Agile Navigation for Quadrupedal Robotscited by 152
  8. 2023Robot Parkour Learningcited by 149
  9. 2022Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotioncited by 88
  10. 2023Barkour: Benchmarking Animal-level Agility with Quadruped Robotscited by 41
  11. 2025Towards Embodiment Scaling Laws in Robot Locomotioncited by 16
  12. 2024Stage-Wise Reward Shaping for Acrobatic Robots: A Constrained Multi-Objective Reinforcement Learning Approachcited by 13
3 more
  1. 2024Robust Ladder Climbing with a Quadrupedal Robotcited by 10
  2. 2023RL-X: A Deep Reinforcement Learning Library (not only) for RoboCupcited by 8
  3. 2025Robust Localization, Mapping, and Navigation for Quadruped Robotscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Rapid Embodiment Adaptation for Quadrupedal Locomotioncited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Neuromorphic Reinforcement Learning for Quadruped Locomotion Control on Uneven Terrain
  2. 2026Agile perceptive multi-skill locomotion for quadrupedal robots in the wild
  3. 2025Acrobotics: A Generalist Approach to Quadrupedal Robots' Parkour
  4. 2025MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion
  5. 2024Reinforcement Learning For Quadrupedal Locomotion: Current Advancements And Future Perspectives
  6. 2025PALo: Learning Posture-Aware Locomotion for Quadruped Robots
  7. 2023Learning Agile Locomotion on Risky Terrains
  8. 2026Towards Torque-Driven Reinforcement Learning for Quadruped Locomotion