The recent advancements in robot manipulation and perception have shown a significant shift towards more efficient and robust policy generation and generalization. A notable trend is the integration of flow-based models and consistency matching techniques, which have demonstrated substantial improvements in inference speed and task success rates. These methods, such as consistency flow matching, are refining the dynamics of policy generation by normalizing velocity fields, thereby enabling single-step inference and enhancing efficiency. Additionally, the use of salient points and hybrid action spaces in imitation learning is proving effective in improving generalization across varied tasks and environments. This approach leverages multimodal observations and different action representations to tackle complex tasks in a sample-efficient manner. Furthermore, the introduction of prescriptive point priors for policy learning is enhancing out-of-distribution generalization by constructing unique state representations. This is achieved through human-annotated points that are propagated through datasets, leading to significant improvements in policy robustness. Another area of progress is the development of contractive dynamical systems for imitation learning, which ensure reliable policy rollouts and efficient out-of-sample recovery. This framework guarantees convergence regardless of perturbations, providing theoretical reliability and empirical performance improvements. Lastly, advancements in robotic perception under severe lighting conditions are being addressed through normalizing flow gradients, which optimize local regions rather than entire images, resulting in higher success rates for object detection tasks. Overall, these innovations are pushing the boundaries of what is possible in robot learning, focusing on efficiency, robustness, and generalization.