论文标题
刚性动态的自动分化和连续敏感性分析
Automatic Differentiation and Continuous Sensitivity Analysis of Rigid Body Dynamics
论文作者
论文摘要
实现智能行为的关键要素是物理理解，它使机器人能够在动态环境中推理其行动的影响。已经提出了几种方法来从基于模型的控制算法的数据中学习动态模型。尽管这种基于学习的方法可以模拟本地观察到的行为，但它们无法推广到更复杂的动态和长期范围。 在这项工作中，我们引入了一个可区分的物理模拟器，以实现刚体动态。利用各种技术进行微分方程集成和梯度计算，我们比较了参数估计的不同方法，这些方法使我们能够推断与物理系统估计和控制的模拟参数。在轨迹优化的背景下，我们引入了一种闭环模型预测控制算法，该算法通过经验来渗透模拟参数，同时实现成本最小化的性能。
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment. Several methods have been proposed to learn dynamics models from data that inform model-based control algorithms. While such learning-based approaches can model locally observed behaviors, they fail to generalize to more complex dynamics and under long time horizons. In this work, we introduce a differentiable physics simulator for rigid body dynamics. Leveraging various techniques for differential equation integration and gradient calculation, we compare different methods for parameter estimation that allow us to infer the simulation parameters that are relevant to estimation and control of physical systems. In the context of trajectory optimization, we introduce a closed-loop model-predictive control algorithm that infers the simulation parameters through experience while achieving cost-minimizing performance.
