Recent advancements in robotics AI are increasingly focused on enhancing the generalization and efficiency of robotic manipulation tasks. Researchers are developing frameworks that integrate multimodal inputs, such as vision and tactile feedback, to improve robots' ability to understand and interact with complex environments. For instance, new models are addressing the challenge of "Information Collapse," where robots overly rely on visual cues, by incorporating language instructions into their decision-making processes. This shift is crucial for enabling robots to follow diverse commands in real-world settings. Additionally, innovative training methods, such as using world models for reinforcement learning, are showing promise in reducing the reliance on extensive expert demonstrations and improving real-world performance. These developments not only enhance the capabilities of robots in tasks like bimanual coordination but also pave the way for more efficient and adaptable systems that can operate effectively in dynamic environments, potentially transforming applications in industries ranging from manufacturing to healthcare.