论文标题
将生物力学模型从Opensim转换为Mujoco
Converting Biomechanical Models from OpenSim to MuJoCo
论文作者
论文摘要
Opensim是一种广泛使用的生物力学模拟器，具有几种解剖学精确的人类肌肉骨骼模型。尽管Opensim提供了分析人类运动的有用工具，但它的速度不够快，无法常规地用于新兴的研究方向，例如通过深层神经网络和增强学习（RL）来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是机器学习研究中的事实模拟器，本身缺乏准确的肌肉骨骼骨骼模型。我们表明，通过一些简单的解剖细节近似值，可以将OpenSIM模型自动转换为Mujoco版本，该版本的运行速度更快600倍。我们还展示了一种计算方法优化Mujoco模型参数的方法，以便两个模拟器的正向模拟产生相似的结果。
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.
