Object-Goal Navigation

Object-goal navigation asks a robot to locate and reach a specified object, often from a short language instruction and with only partial views of its surroundings. Approaches include reinforcement-learned policies, semantic maps and frontier exploration, and vision-language or language-model agents that plan hierarchically; many also use spatial memory to avoid repeated searches. Open-vocabulary targets, persistent maps, and real-robot tests are broadening the task beyond fixed object categories and static simulations. A central challenge is balancing reliable target grounding and safe, efficient movement with limited compute, especially when objects move, scenes change, or the robot must verify that it has truly reached the target.