The field of robotic manipulation and control is rapidly advancing, with a focus on developing systems that can learn efficiently from minimal human input and adapt to real-world uncertainties and diverse embodiments. Recent research has explored the use of vision-language grounding, task-aware decomposition, and hybrid diffusion models to enable generalizable bimanual manipulation and long-horizon task planning. Notable papers in this area include VLBiMan, which introduces a framework for deriving reusable skills from a single human example, and Hybrid Diffusion, which proposes a novel mix of discrete variable diffusion and continuous diffusion for simultaneous symbolic and continuous planning. Other noteworthy papers include DemoGrasp, which presents a simple yet effective method for learning universal dexterous grasping, and Super-Mimic, which enables zero-shot robotic imitation by directly inferring procedural intent from unscripted human demonstration videos. These advances have the potential to significantly improve the capabilities of robotic systems and enable more efficient and effective manipulation and control.