Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into lan- guage systems for tactile Commonsense Rea- soning, scaling such systems to realistic open- world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, pro- viding insufficient supervision for reasoning from tactile observations to physical common- sense and hindering the learning of transferable tactile commonsense; (2) Tactile signals are inherently redundant and action-specific, yet existing methods often overlook these proper- ties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile-language framework that scales tac- tile commonsense reasoning to the open world from both data and representation perspec- tives. First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering 410 objects, 8 scenarios, and 7 sensor types, providing a solid data founda- tion for open-world generalization. We fur- ther introduce TouchThinker-Bench, an open- world benchmark with more realistic and di- verse tasks. Then, we further introduce action- aware tactile modeling and a two-stage training paradigm to improve tactile representation effi- ciency and enable efficient reasoning. Experi- mental results demonstrate that TouchThinker achieves competitive performance against state- of-the-art models across multiple datasets. Our code and dataset will be released.

Image-goal navigation steers an agent to a target location specified by an image in unseen environments. Existing methods primarily handle this task by learning an end-to-end navigation policy, which compares the similarities of target and observation images and directly predicts the actions. However, when the target is distant or lies in another room, such methods fail to extract informative visual cues, leading the agent to wander around. Motivated by the human cognitive principle that deliberate, high-level reasoning guides fast, reactive execution in complex tasks, we propose Hierarchical Reasoning Navigation (HRNav), a framework that decomposes image-goal navigation into high-level planning and low-level execution. In high-level planning, a vision-language model is trained on a self-collected dataset to generate a short-horizon plan, such as whether the agent should walk through the door or down the hallway. This downgrades the difficulty of the long-horizon task, making it more amenable to the execution part. In low-level execution, an online reinforcement learning policy is utilized to decide actions conditioned on the short-horizon plan. We also devise a novel Wandering Suppression Penalty (WSP) to further reduce the wandering problem. Together, these components form a hierarchical framew ork for Image-Goal Navigation. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method.