The field of robotics is moving towards more advanced and autonomous systems, with a focus on manipulation and interaction with complex environments. Recent developments have centered around improving the ability of robots to learn and adapt to new situations, with a emphasis on real-time control and decision-making. One of the key areas of research is in the development of novel control frameworks and algorithms that can handle complex and uncertain environments, such as those found in real-world scenarios. Additionally, there is a growing interest in the use of machine learning and data-driven approaches to improve the efficiency and effectiveness of robotic systems. Noteworthy papers include: DyWA, which proposes a novel framework for non-prehensile manipulation that enhances action learning by jointly predicting future states while adapting to dynamics variations. ManipTrans, which introduces a novel two-stage method for efficiently transferring human bimanual skills to dexterous robotic hands in simulation. Bayesian Inferential Motion Planning, which investigates motion planning through Bayesian inference and proposes the use of heavy-tailed distributions to enhance probabilistic inferential search for motion plans.