OpenSim is a widely used biomechanics simulator with several anatomically accurate human musculo-skeletal models. While OpenSim provides useful tools to analyse human movement, it is not fast enough to be routinely used for emerging research directions, e.g., learning and simulating motor control through deep neural networks and Reinforcement Learning (RL). We propose a framework for converting OpenSim models to MuJoCo, the de facto simulator in machine learning research, which itself lacks accurate musculo-skeletal human models. We show that with a few simple approximations of anatomical details, an OpenSim model can be automatically converted to a MuJoCo version that runs up to 600 times faster. We also demonstrate an approach to computationally optimize MuJoCo model parameters so that forward simulations of both simulators produce similar results.

中文翻译：

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将生物力学模型从 OpenSim 转换为 MuJoCo

OpenSim 是一种广泛使用的生物力学模拟器，具有多个解剖学上准确的人体肌肉骨骼模型。虽然 OpenSim 提供了有用的工具来分析人体运动，但它的速度不足以常规用于新兴的研究方向，例如通过深度神经网络和强化学习 (RL) 学习和模拟运动控制。我们提出了一个将 OpenSim 模型转换为 MuJoCo 的框架，MuJoCo 是机器学习研究中事实上的模拟器，它本身缺乏准确的肌肉骨骼人体模型。我们表明，通过解剖细节的一些简单近似，OpenSim 模型可以自动转换为运行速度提高 600 倍的 MuJoCo 版本。