Simultaneous Localization and Mapping (SLAM)

Simulation-to-Real Transfer (Sim2Real) involves developing AI agents in virtual environments and then applying their learned skills to real-world robots. This approach accelerates training, reduces costs, and enhances safety. Key techniques include domain randomization and transfer learning, which help bridge the gap between simulated and physical conditions, ensuring that the AI performs reliably outside the virtual setting.