Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images. Even when conditioned on actions, purely deep learning based architectures typically lack a physically interpretable latent space. In this study, we use a differentiable physics engine within an action-conditional video representation network to learn a physical latent representation. We propose supervised and self-supervised learning methods to train our network and identify physical properties. The latter uses spatial transformers to decode physical states back into images. The simulation scenarios in our experiments comprise pushing, sliding and colliding objects, for which we also analyze the observability of the physical properties. In experiments we demonstrate that our network can learn to encode images and identify physical properties like mass and friction from videos and action sequences in the simulated scenarios. We evaluate the accuracy of our supervised and self-supervised methods and compare it with a system identification baseline which directly learns from state trajectories. We also demonstrate the ability of our method to predict future video frames from input images and actions.

本研究提出使用可微的物理引擎在行动条件下的视频表示网络中学习物理潜在表示。我们使用监督和自监督学习方法来训练我们的网络，并确定物理属性。实验中，我们演示了我们的网络可以从视频和行动序列中学习编码图像并识别物理属性，我们还比较了我们的受监督和自我监督方法的准确性，并展示了我们的方法预测未来视频帧的能力。

使用可微分物理学从视频中学习识别物理参数