Real-to-Sim via End-to-End Differentiable Simulation and Rendering

Identifying predictive world models for robots in novel environments from sparse online observations is essential for robot task planning and execution in novel environments. However, existing methods that leverage differentiable simulators to identify world models are incapable of jointly optimizing the shape, appearance, and physical properties of the scene. In this work, we introduce a novel object representation that allows the joint identification of these properties. Our method employs a novel differentiable point-based object representation coupled with a grid-based appearance field, which allows differentiable object collision detection and rendering. Combined with a differentiable physical simulator, we achieve end-to-end optimization of world models, given the sparse visual and tactile observations of a physical motion sequence. Through a series of benchmarking system identification tasks in simulated and real environments, we show that our method can learn both simulation- and rendering-ready world models from only a few partial observations.