用物理仿真训练能自适应走路速度的数字人模型
Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation
- 用生成数据+对抗模仿学习训练骨骼人形模型
- 不同速度下误差仅5.24±0.09度，运动真实
- 适合做康复、外骨骼设计的虚拟实验
虚拟人体步态模型（数字孪生）可避免繁琐的数据采集，但存在仿真到现实的差距以及对多种行走条件适应性差的问题。为此，我们构建并验证了一个框架，使骨骼化人形代理能够自适应不同行走速度，同时保持生物力学上的合理性。该框架结合了从开源生物力学数据生成符合生物力学的步态运动学的合成数据生成器，以及利用对抗式模仿学习训练代理行走策略的训练系统。通过对比分析代理的运动学、合成数据与原始生物力学数据，结果表明代理在不同速度下的均方根误差为5.24±0.09度，展现出良好适应能力。本研究为构建人体运动数字孪生迈出重要一步，有望应用于生物力学研究、外骨骼设计和康复领域。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。