Synthetic Data Generation for Physical AI

Physical AI models allow autonomous systems to perceive, understand, interact with, and navigate the physical world. Synthetic data is critical for training and testing physical AI models.

World models utilize diverse input data, including text, images, videos, and movement information, to generate and simulate virtual worlds with remarkable accuracy.   

World models are characterized by their exceptional generalization capabilities, requiring minimal fine-tuning for various applications. They serve as the cognitive engines for robots and autonomous vehicles, leveraging their comprehensive understanding of real-world dynamics. To achieve this level of sophistication, world models rely on vast amounts of training data. 

World model development benefits significantly from generating infinite synthetic data through physically accurate simulations. This approach not only accelerates the model training process but also enhances a model’s ability to generalize across diverse scenarios. Domain randomization techniques further augment this process by allowing for the manipulation of numerous parameters such as lighting, background, color, location, and environment—variations that would be nearly impossible to capture comprehensively from real-world data alone.

Robot learning encompasses a range of algorithms and methodologies that enable a robot to acquire new skills, including manipulation, locomotion, and classification, in either simulated or real-world environments. Reinforcement learning, imitation learning, and diffusion policy are the key methodologies applied to train robots.

One important skill for robots is manipulation—picking up, sorting, and assembling items—similar to what you see in factories. Real-world human demonstrations are typically used as inputs for training. However, collecting a large and diverse dataset is quite expensive.

To overcome this challenge, developers can utilize the NVIDIA Isaac GR00T-Mimic and GR00T-Dreams blueprints, built on NVIDIA Cosmos™, to generate large, diverse synthetic motion datasets for training.

The NVIDIA Isaac GR00T-Dreams blueprint generates vast amounts of synthetic trajectory data using Cosmos, prompted by a single image and language instructions. This enables robots to learn new tasks in unfamiliar environments without needing specific teleoperation data.

The NVIDIA Isaac GR00T-Mimic blueprint generates vast amounts of synthetic trajectory data from just a handful of human demonstrations. This enables robots to improve their manipulation across a known task and environment.

These datasets can then be used to train the Isaac GR00T open foundation models within Isaac Lab, enabling generalized humanoid reasoning and robust skill acquisition.

Software-in-loop (SIL) testing is a crucial stage for AI-powered robots and autonomous vehicles, where the control software is evaluated in a simulated environment rather than on real hardware.

Synthetic data generated from simulation ensures accurate modeling of real-world physics, including sensor inputs, actuator dynamics, and environmental interactions. This also provides a way to capture rare scenarios that are dangerous to collect in the real world. This ensures that the robot software stack in simulation behaves as it would on the physical robot, allowing for thorough testing and validation without the need for physical hardware.  

Synthetic data from these simulations is fed back into the robot brains. The robot brains perceive the results, deciding the next action. This cycle continues with Mega precisely tracking the state and position of all the assets in the digital twin.