We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. Starting from the initial observed state, object behavior is simulated forward in time. Based on the simulated and observed behavior, the correction estimator then determines refined physical parameters for each object. The method can be iterated for increased precision. Our approach is generic, as it allows for the use of an arbitrary - not necessarily differentiable - physics engine and correction estimator. For the latter, we evaluate both gradient-free hyperparameter optimization and a deep convolutional neural network. We demonstrate faster and more robust convergence of the learned method in several simulated 2D scenarios focusing on bin situations.
翻译:我们提出了一个新的方法来估计视频中物体的物理属性。 我们的方法包括一个物理引擎和一个校正估计器。 从最初观测到的状态开始, 物体的行为会被及时模拟。 根据模拟和观察的行为, 校正估计器然后确定每个物体的精细物理参数。 方法可以循环, 以便提高精确度。 我们的方法是通用的, 因为它允许使用任意的, 不一定不同 的物理引擎和校正估计器。 对于后者, 我们评估的是无梯度超光度优化和深相向神经网络。 我们在一些模拟的 2D 情景中, 我们展示了学习方法的更快和更加牢固的趋同, 其重点是对硬体环境的模拟 2D 情景 。