The field of robotic manipulation is rapidly advancing, with a focus on developing innovative solutions for complex tasks such as non-prehensile manipulation, deformable object handling, and multi-robot collaboration. Recent developments have seen the integration of generative models, reinforcement learning, and sensor fusion to improve the robustness and efficiency of robotic systems. Notably, researchers are exploring the use of multimodal sensing, including vision, force, and proprioception, to enhance contact-rich manipulation capabilities. Furthermore, the development of simulation environments and theoretical frameworks for closed-loop stability is enabling the creation of more sophisticated and adaptive robotic systems. Noteworthy papers include: Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation, which presents a unified framework for multi-robot manipulation. Sashimi-Bot: Autonomous Tri-manual Advanced Manipulation and Cutting of Deformable Objects, which demonstrates a milestone in robotic manipulation of deformable objects.