The field of robotic manipulation and control is moving towards the integration of vision-language models to improve task execution and failure detection. Recent developments have focused on creating more efficient and scalable models that can handle complex tasks and generalize to new environments. Noteworthy papers in this area include: I-FailSense, which proposes a method for detecting semantic misalignment errors in robotic manipulation tasks. ComputerAgent, which introduces a lightweight hierarchical reinforcement learning framework for controlling desktop applications. VLAC, which presents a general process reward model for robotic real-world reinforcement learning. These papers demonstrate the potential of vision-language models to advance the field of robotic manipulation and control.