The field of robot learning and control is rapidly advancing, with a focus on developing more efficient, scalable, and generalizable methods. Recent research has explored the use of pretraining, transfer learning, and multi-modal representations to improve robot performance in complex tasks. Notably, the development of controllable world models, such as Ctrl-World, has enabled more effective evaluation and improvement of generalist robot policies. Additionally, the introduction of novel attention mechanisms, like A3RNN, has enhanced the ability of robots to focus on relevant aspects of their environment. Other significant advancements include the proposal of unified continuous and discrete representation learning frameworks, such as UniCoD, and the development of hierarchical evaluation paradigms, like RoboHiMan, for compositional generalization in long-horizon manipulation tasks. Some noteworthy papers include: OmniSAT, which introduces a compact action tokenization method for efficient auto-regressive modeling. Ctrl-World, which presents a controllable generative world model for evaluating and improving generalist robot policies. Actron3D, which enables robots to acquire transferable 6-DoF manipulation skills from few human videos.