The field of robotics is rapidly advancing with the development of more sophisticated vision-language-action (VLA) models and robot learning algorithms. Recent research has focused on improving the generalization capabilities of VLA models, enabling them to perform effectively in diverse environments and situations. This has been achieved through the use of procedurally generated environments, multi-coordinate elastic maps, and embodiment scaling laws. Additionally, there have been significant advancements in offline reinforcement learning, with the introduction of methods such as Model-Based ReAnnotation and Video-Enhanced Offline RL. These approaches have shown promising results in improving the efficiency and effectiveness of robot learning. Noteworthy papers include Learning to Drive Anywhere with Model-Based Reannotation, which demonstrates state-of-the-art performance in navigation tasks, and UniVLA, which achieves superior performance over existing VLA models with less pretraining compute and downstream data. Overall, the field is moving towards more generalizable and efficient robot learning algorithms, with potential applications in areas such as robotic manipulation, autonomous driving, and open-world robot navigation.