The field of robot learning and simulation is rapidly advancing, with a focus on developing more efficient and effective methods for training robots to perform complex tasks. One of the key areas of research is the development of open-source frameworks that can simplify the process of collecting data, training policies, and deploying robots in real-world environments. Another important area of research is the generation of realistic videos and simulations that can be used to train robots and evaluate their performance. Recent works have also explored the use of multimodal generation and simulation to learn multimodal policies that can be transferred to real-world scenarios. Overall, the field is moving towards more integrated and comprehensive approaches that can accelerate the development and deployment of autonomous robots. Notable papers include: Ark, which introduces an open-source Python-based framework for robot learning, and RoboEnvision, which proposes a novel pipeline for generating long-horizon videos for robotic manipulation tasks. Epona and RIGVid also present innovative approaches to autoregressive diffusion world modeling and robotic manipulation by imitating generated videos, respectively.