The field of robotic manipulation and navigation is rapidly advancing, with a focus on developing more efficient, robust, and adaptive systems. Recent research has explored the use of reinforcement learning, imitation learning, and other techniques to improve the ability of robots to manipulate and navigate complex environments. One key area of research is the development of systems that can learn from few demonstrations and adapt to new situations, such as the use of entropy-based frameworks for dynamic object manipulation. Another area of focus is the development of systems that can navigate and manipulate in dynamic environments, such as the use of visual affordances and manipulability priors for mobile manipulation. Notable papers in this area include Actor-Critic for Continuous Action Chunks, which introduces a novel RL framework for long-horizon robotic manipulation, and Manipulate-to-Navigate, which proposes a reinforcement learning-based approach for learning manipulation actions that facilitate subsequent navigation. Overall, these advances have the potential to enable more efficient and effective robotic manipulation and navigation in a wide range of applications.