论文标题
可区分和可学习的机器人模型
Differentiable and Learnable Robot Models
论文作者
论文摘要
构建对物理过程的可区分模拟最近受到了越来越多的关注。具体而言，某些努力开发了可区分的机器人物理引擎，这些引擎是由将刚体模拟与现代可区分的机器学习库合并的计算益处的动机。在这里，我们提出了一个库，该库专注于将数据驱动方法与分析刚体计算相结合的能力。更具体地说，我们的库\ emph {可区分的机器人模型}实现了\ emph {dissionable}和\ emph {可学习}模型的机器人在Pytorch中的机器人和动力学。源代码可在\ url {https://github.com/facebookresearch/differentiable-robot-model}中获得
Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulations with modern differentiable machine learning libraries. Here, we present a library that focuses on the ability to combine data driven methods with analytical rigid body computations. More concretely, our library \emph{Differentiable Robot Models} implements both \emph{differentiable} and \emph{learnable} models of the kinematics and dynamics of robots in Pytorch. The source-code is available at \url{https://github.com/facebookresearch/differentiable-robot-model}
