Image-Goal Navigation
Overview
Image-goal navigation studies how an embodied agent can reach a place or object shown in a goal image, often without coordinates or a prebuilt map. Methods range from goal-conditioned reinforcement-learning policies and topological mapping to vision-language planning and world models that predict future observations or action sequences; geometric localization and local planners help execute safely. Systems are evaluated in simulated indoor environments such as Habitat and on physical ground and aerial robots, with measures including success rate, path efficiency, localization error, and collisions. A central challenge is balancing robust long-horizon navigation and precise arrival with real-time inference, uncertainty handling, and safe transfer from simulation to the real world.
Latest papers
17 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.