Real to Sim

Real-to-sim (simulation-to-reality) research aims to bridge the gap between simulated and real-world robotic environments, enabling the training of robust and reliable robot control policies in simulation for deployment in the real world. Current efforts focus on improving the realism of simulations, developing techniques for transferring learned policies (often using reinforcement learning and neural networks like GRUs and recurrent policy optimization), and addressing uncertainties inherent in the sim-to-real transfer process through methods such as privileged training and Bayesian inference. This work is crucial for accelerating robotics development, reducing the cost and risk associated with real-world training, and ultimately enabling more capable and adaptable robots across various applications.