Human Imitated Bipedal Locomotion with Frequency Based Gait Generator Network
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911278841200640 |
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| author | Ates, Yusuf Baran Morgul, Omer |
| author_facet | Ates, Yusuf Baran Morgul, Omer |
| contents | Learning human-like, robust bipedal walking remains difficult due to hybrid dynamics and terrain variability. We propose a lightweight framework that combines a gait generator network learned from human motion with Proximal Policy Optimization (PPO) controller for torque control. Despite being trained only on flat or mildly sloped ground, the learned policies generalize to steeper ramps and rough surfaces. Results suggest that pairing spectral motion priors with Deep Reinforcement Learning (DRL) offers a practical path toward natural and robust bipedal locomotion with modest training cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17387 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Human Imitated Bipedal Locomotion with Frequency Based Gait Generator Network Ates, Yusuf Baran Morgul, Omer Robotics Learning human-like, robust bipedal walking remains difficult due to hybrid dynamics and terrain variability. We propose a lightweight framework that combines a gait generator network learned from human motion with Proximal Policy Optimization (PPO) controller for torque control. Despite being trained only on flat or mildly sloped ground, the learned policies generalize to steeper ramps and rough surfaces. Results suggest that pairing spectral motion priors with Deep Reinforcement Learning (DRL) offers a practical path toward natural and robust bipedal locomotion with modest training cost. |
| title | Human Imitated Bipedal Locomotion with Frequency Based Gait Generator Network |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.17387 |