The field of robotics is witnessing significant advancements in visuomotor policy learning and robot manipulation. Recent developments focus on improving the robustness and efficiency of visuomotor policies, enabling robots to perform complex tasks with increased reliability and adaptability. Notable innovations include the use of latent policy barriers, adaptive diffusion policies, and causal structure distributions to enhance policy learning and generalization. Additionally, researchers are exploring novel approaches to robot manipulation, such as decoupled diffusion frameworks and segmentation-driven actor-critic methods, to improve task execution and visual generalization. These advancements have the potential to significantly impact various applications, including robotic assembly, manipulation, and human-robot interaction. Noteworthy papers in this area include Latent Policy Barrier, which introduces a framework for robust visuomotor policy learning, and ADPro, which proposes a test-time adaptive diffusion policy for robot manipulation. Other notable papers include Learning Causal Structure Distributions for Robust Planning, D3P, and CoopDiff, which present innovative approaches to visuomotor policy learning and robot manipulation.