In recent months, the field of robotics has witnessed remarkable advancements, largely propelled by the rapid progression in generative artificial intelligence. Leading tech companies and research labs are using generative AI models to address some of the big challenges in robotics that have so far prevented them from being widely deployed outside of heavy industry and in research labs. Training robotic machine learning models in real-world scenarios presents a host of challenges. The process is slow, unfolding at the pace of real-time events. It’s also costly, constrained by the number of robots that can be physically deployed. Furthermore, safety concerns and limited access to diverse environments for comprehensive training pose additional hurdles. To circumvent these obstacles, researchers use simulated environments for training robotic models. This approach allows for scalability and significantly reduces costs compared to real-world training. However, this solution isn’t without its drawbacks. Creating detailed simulated environments can be costly. Moreover, these environments often lack the intricate details found in the real world, leading to a disparity known as the “sim-to-real gap.” This gap results in a performance drop when models trained in simulation are deployed in the real world, as they can’t handle the complexities and nuances of their environments.

Full explainer : How generative AI is making robots smarter, more capable, and more ready for the mainstream.