Imagine teaching a robot a new task in a completely unfamiliar environment. Traditionally, this would require extensive retraining and reprogramming. But what if the robot could leverage its experience from other settings, applying previously learned knowledge to quickly master new challenges? This is the promise of EnvBridge, a groundbreaking new approach to embodied AI that's changing the game in robotics. Researchers have long grappled with the limitations of robots operating in novel environments. Existing methods often struggle with transferring knowledge learned in one setting to another, leading to inefficient training processes and limited real-world applicability. EnvBridge tackles this challenge head-on by introducing the concept of 'cross-environment knowledge transfer.' EnvBridge enables robots to access and utilize successful control codes from previous experiences in different environments. Instead of starting from scratch each time, the robot can retrieve relevant code snippets from its memory and adapt them to the new context. This innovative approach allows the robot to learn more quickly and efficiently, effectively bridging the gap between diverse environments. The EnvBridge system consists of three core components: Code Generation, Memory Retrieval, and Re-Planning with Transferred Knowledge. Initially, the robot attempts to generate control code based on the new task and environment. If this initial attempt fails, EnvBridge retrieves similar, successful code from its memory. Then, using a process called Knowledge Transfer, the retrieved code is adapted to the target environment's specifics, such as coordinates and scale. Finally, the robot utilizes this adapted code for Re-Planning, effectively incorporating previously learned insights to improve its performance. Tested across three diverse robotics benchmarks (RLBench, MetaWorld, and CALVIN), EnvBridge demonstrated remarkable adaptability and performance improvement. It consistently outperformed baseline methods, showcasing its ability to leverage knowledge across different environments and task instructions. The research found that EnvBridge was particularly effective in tackling tasks that traditional methods struggled with, highlighting the potential of cross-environment learning. While EnvBridge represents a significant step forward in embodied AI, challenges remain. Managing the ever-growing memory efficiently, incorporating diverse data like images and sounds, and adapting to continuously evolving environments are key areas for future research. However, EnvBridge’s innovative approach to knowledge transfer paves the way for more adaptable, efficient, and robust robots capable of operating effectively in the complexities of the real world.