Differentiable physics is a powerful tool in computer vision and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are known in advance. In this paper, we propose a novel approach to differentiable physics with frictional contacts which represents object shapes implicitly using signed distance fields (SDFs). Our simulation supports contact point calculation even when the involved shapes are nonconvex. Moreover, we propose ways for differentiating the dynamics for the object shape to facilitate shape optimization using gradient-based methods. In our experiments, we demonstrate that our approach allows for model-based inference of physical parameters such as friction coefficients, mass, forces or shape parameters from trajectory and depth image observations in several challenging synthetic scenarios and a real image sequence.

翻译：差异物理学是计算机视觉和机器人的强大工具,有助于对相互作用进行现场理解和推理; 现有方法通常局限于以预知的简单形状或形状为对象的物体; 在本文中,我们提出了对摩擦接触的可区别物理学的新办法,这种摩擦接触代表了使用签名的距离场(SDFs)暗含的物体形状; 我们的模拟支持联络点计算, 即使所涉形状是非曲线的。 此外, 我们提出了区分物体形状的动态的方法, 以便于使用梯度法优化形状。 我们的实验表明, 我们的方法允许以模型为基础推断物理参数, 如摩擦系数、质量、力量或形状参数, 以轨迹和深度图像观测为基础, 在若干具有挑战性的合成情景和真实图像序列中。