Sim2Real Transfer

Sim2Real transfer aims to bridge the gap between simulated and real-world environments for training robotic agents, enabling cost-effective development and deployment. Current research focuses on improving the realism of simulators, often employing techniques like Gaussian splatting for enhanced visual fidelity, bird's-eye-view representations for robust navigation, and domain randomization to increase the robustness of learned policies. Successful sim2real transfer holds significant promise for accelerating the development of autonomous systems across various domains, from robotic manipulation and autonomous driving to more complex tasks involving human-robot interaction.