1、GEN AI-POWEREDSYNTHETIC DATAGENERATION FOR ROBOTICSFor advancing AI in robotics by creating realistic,scalable,and diverse training datasetsWhite Paper Gen AI-powered SDG in Robotics2Accurate sensor simulation,which replicates real-world sensoryinputs.Dynamic environmental interactions allow AI mode

2、ls to respond torealistic changes.Complex acoustic or magnetic modeling supporting advanced usecases.Collecting real-world robotics data is time-consuming,expensive,and fraught withsafety and environmental concerns.Synthetic data,generated through physicallyaccurate simulations,offers a scalable and

3、 cost-effective alternative to real-worlddata collection.Researchers and engineers can leverage the NVIDIA Omniverse platform and itsassociated frameworks and reference applications,such as NVIDIA Replicator,NVIDIA Warp,and NVIDIA Isaac Sim,to create physics-based,AI-enabled 3Denvironments that accu

4、rately mimic reality.These physics-based environmentsenable the generation of diverse datasets for training and validating AI models,equipping them to handle everything from routine tasks to rare,hard-to-replicatescenarios.Beyond visual data,physical modeling and simulations are powerful tools forge

5、nerating non-visual synthetic data,which is essential for training,testing,andvalidating Physical AI systems in robotics,autonomous dynamical systems,Industry4.0,and the internet of things(IoT).Non-visual data includes sensory inputs that gobeyond visual imagery,such as temperature,pressure,acoustic

6、 signals,force/torquedata,and electromagnetic fields.Synthetic datasets enhance model generalization,robustness,and efficiency throughaccurate simulations.The development of more intelligent and safer robots capableof navigating unpredictable environments is achieved through the followingadvancement