Object goal navigation (ObjectNav) is a fundamental task in embodied AI, requiring an agent to locate a target object in previously unseen environments. This task is particularly challenging because it requires both perceptual and cognitive processes, including object recognition and decision-making. While substantial advancements in perception have been driven by the rapid development of visual foundation models, progress on the cognitive aspect remains constrained, primarily limited to either implicit learning through simulator rollouts or explicit reliance on predefined heuristic rules. Inspired by neuroscientific findings demonstrating that humans maintain and dynamically update fine-grained cognitive states during object search tasks in novel environments, we propose CogNav, a framework designed to mimic this cognitive process using large language models. Specifically, we model the cognitive process using a finite state machine comprising fine-grained cognitive states, ranging from exploration to identification. Transitions between states are determined by a large language model based on a dynamically constructed heterogeneous cognitive map, which contains spatial and semantic information about the scene being explored. Extensive evaluations on the HM3D, MP3D, and RoboTHOR benchmarks demonstrate that our cognitive process modeling significantly improves the success rate of ObjectNav at least by relative 14% over the state-of-the-arts.

A pairwise dictionary selected learning (PDSL) model is proposed in this study, which is specially tailored to synthesise a low‐resolution image. This is accomplished with the use of an external dictionary of high‐resolution images, which is selectively learned based on the internal dictionary of the target reconstruction image. The PDSL can avoid interpolating the fictitious information to the reconstructed image. Optimisation is performed using a bilateral regularisation term in an edge reserving based on the aid of spatial and directional proximity. Using the proposed approach, the results achieved are superior to the existing dictionary learning‐based methods. The authors also present a quantitative evaluation of super‐resolution reconstruction using various statistics which demonstrated significant average peak signal‐to‐noise ratio improvements by their model. A comparison of the proposed method with five other state‐of‐the‐art methods is presented and the authors’ method achieves better visual effects in edge structures.