Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916508948496384 |
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| author | Chiu, Yi-Hung Lee, Ung Hee Song, Changseob Hu, Manaen Kang, Inseung |
| author_facet | Chiu, Yi-Hung Lee, Ung Hee Song, Changseob Hu, Manaen Kang, Inseung |
| contents | Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy. We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24 +- 0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03949 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation Chiu, Yi-Hung Lee, Ung Hee Song, Changseob Hu, Manaen Kang, Inseung Robotics Machine Learning Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as the sim-to-real gap and limited adaptability to diverse walking conditions persist. To address these, we developed and validated a framework to create a skeletal humanoid agent capable of adapting to varying walking speeds while maintaining biomechanically realistic motions. The framework combines a synthetic data generator, which produces biomechanically plausible gait kinematics from open-source biomechanics data, and a training system that uses adversarial imitation learning to train the agent's walking policy. We conducted comprehensive analyses comparing the agent's kinematics, synthetic data, and the original biomechanics dataset. The agent achieved a root mean square error of 5.24 +- 0.09 degrees at varying speeds compared to ground-truth kinematics data, demonstrating its adaptability. This work represents a significant step toward developing a digital twin of human locomotion, with potential applications in biomechanics research, exoskeleton design, and rehabilitation. |
| title | Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2412.03949 |