gradSim：用于系统辨识和可视化控制的可微分仿真
gradSim: Differentiable simulation for system identification and visuomotor control
摘要：
我们考虑的问题，估计一个对象的物理特性，如质量，摩擦力和弹性直接从视频序列。这种系统辨识问题由于在图像形成期间的信息损失而从根本上是不适定的。目前的解决方案需要精确的3D标签，这是劳动密集型的收集，并且不可行的许多系统，如可变形的固体或布创建。我们提出gradSim，一个框架，克服了依赖于3D监督，利用微分多物理场仿真和微分渲染共同模拟场景动态和图像形成的演变。这种新颖的组合使得能够从视频序列中的像素反向传播到生成它们的底层物理属性。此外，我们的统一计算图-从动态到渲染过程-能够在具有挑战性的视觉控制任务中进行学习，而不依赖于基于状态的（3D）监督，同时获得与依赖于精确3D标签的技术竞争或更好的性能。
英文摘要：
We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally ill-posed due to the loss of information during image formation. Current solutions require precise 3D labels which are labor-intensive to gather, and infeasible to create for many systems such as deformable solids or cloth. We present gradSim, a framework that overcomes the dependence on 3D supervision by leveraging differentiable multiphysics simulation and differentiable rendering to jointly model the evolution of scene dynamics and image formation. This novel combination enables backpropagation from pixels in a video sequence through to the underlying physical attributes that generated them. Moreover, our unified computation graph -- spanning from the dynamics and through the rendering process -- enables learning in challenging visuomotor control tasks, without relying on state-based (3D) supervision, while obtaining performance competitive to or better than techniques that rely on precise 3D labels.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
