Generative Control

Generative control focuses on using generative models, such as diffusion models and variational autoencoders (VAEs), to create controllers for complex systems. Current research emphasizes efficient closed-loop control, leveraging techniques like asynchronous denoising and fast sampling methods to improve performance and scalability across diverse applications, including robotics, fluid dynamics, and molecular simulations. This approach allows for more nuanced control over complex systems, enabling tasks like generating realistic human-like motions, optimizing molecular configurations, and streamlining simulations, ultimately advancing fields ranging from materials science to artificial intelligence.