Robot Navigation

Robot navigation combines sensing and localization with route planning and motion control, from indoor mobile robots and legged platforms to drones, vehicles, and medical devices. Common approaches include SLAM and geometric planners such as A* and model-predictive control, alongside reinforcement learning, imitation learning, and vision-language policies that use semantic maps, memory, or predicted futures. Methods are tested in simulation and on physical robots, with benchmarks such as Habitat HM3D and MP3D for object navigation and R2R-CE for language-guided routes. A central challenge is making long-horizon decisions that remain safe and efficient when perception is uncertain, environments change, and policies transfer from simulation to real robots.