Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912649002876928 |
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| author | Stasica, Maximilian Bick, Arne Bohlinger, Nico Mohseni, Omid Fritzsche, Max Johannes Alois Hübler, Clemens Peters, Jan Seyfarth, André |
| author_facet | Stasica, Maximilian Bick, Arne Bohlinger, Nico Mohseni, Omid Fritzsche, Max Johannes Alois Hübler, Clemens Peters, Jan Seyfarth, André |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations Stasica, Maximilian Bick, Arne Bohlinger, Nico Mohseni, Omid Fritzsche, Max Johannes Alois Hübler, Clemens Peters, Jan Seyfarth, André Robotics 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. |
| title | Bridge the Gap: Enhancing Quadruped Locomotion with Vertical Ground Perturbations |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.13488 |