In the development of robotics, the gap between simulated environments and the real world has always been a significant challenge. Recently, NVIDIA's GEAR Lab collaborated with a research team from Carnegie Mellon University to develop a new framework called ASAP (Aligning Simulation and Real Physics), aimed at narrowing this gap. The system has made remarkable progress in reducing motion errors between robotic simulations and real-world movements, achieving approximately a 53% reduction in motion errors, which shows a clear advantage over existing methods.
The workflow of the ASAP framework consists of two stages. First, the robot is trained in a virtual environment, and then a special model is used to address the differences in the real world. This model can learn and adjust the variations between virtual and actual movements, enabling more precise action transfer. Through this system, robots can directly transfer complex actions, such as jumping and kicking, from the simulated environment to the real world.
In practical tests, the research team used the Unitree G1 humanoid robot to successfully demonstrate various agile movements, such as jumping over a meter forward. Tests showed that the ASAP system significantly outperformed other existing methods in terms of motion accuracy. To showcase the system's potential, researchers even had the robot mimic the movements of famous athletes like Cristiano Ronaldo, LeBron James, and Kobe Bryant. However, the experiments also revealed some hardware limitations, as the robot's motors often overheated during dynamic movements, and two robots were damaged while collecting data.
The research team stated that this is just the beginning. In the future, the ASAP framework may help robots learn more natural and diverse movements. To encourage more researchers to participate, they have made the code publicly available on GitHub, inviting others to explore and develop further based on this framework.
Key Points:
🌟 The ASAP framework developed by the research team can reduce the error between robotic simulations and real-world movements by approximately 53%.
🤖 By training in simulated environments and using a special model, ASAP can effectively adjust the robot's performance in the real world.
🏀 During tests, the robot successfully mimicked the actions of several sports stars, but issues such as hardware overheating and equipment damage arose during the experiments.
