Bridging Sim2Real Gap: Simulation Tuning for Training Deep Learning Robotic Perception Models

Deep neural networks enable accurate perception for robots. Simulation offers a way to train deep learning robotic perception models that were previously not possible in scenarios where it is prohibitively expensive, time-consuming, or infeasible to collect large labeled datasets. We'll explore the advantages of training robotic perception models with simulated data and the challenges that come with developing a model that will effectively transfer to the real world. We'll dive into how NVIDIA is bridging the gap between simulation and reality with domain randomization, photorealistic simulation, and accurate physics imitation with Isaac Sim. We'll discuss how to properly tune simulation and domain randomization parameters so models may successfully generalize to reality. We'll also reveal findings as to what factors have the greatest impact in generating useful simulated data and how to validate that perception models are learning transferrable representations.