Conditional Diffusion Transformers in Action Prediction

This chapter explores the practical advantages of using a conditional diffusion transformer for predicting future states in models, focusing on the efficiency and accuracy of the predictions. It critiques existing models, delves into the complexities of conditioning actions, and discusses methods for trajectory planning using optimization tools to achieve desired outcomes.