wuyi2121/travexplorer

TravExplorer is a research project from a group at Shanghai Jiao Tong University. The full title in the README is Cross-Floor Embodied Exploration via Traversability-Aware 3D Planning. The authors listed are Han Zheng, Zhe Chen, Yudong Huang, Haoran Liu, Jinghao Wang, Ming Yang, and Tong Qing. It is an academic robotics paper, not a tool that you install and run today. Embodied exploration means a robot moving through a physical space, deciding for itself where to go next, in order to find a goal or build a map. Object navigation is the version of this task where the robot is asked to find a specific object, such as a chair or a fridge. Most prior systems work on a single floor of a building, since reasoning about stairs and floor transitions is much harder. The README describes TravExplorer as the first system to handle both single-floor and cross-floor object navigation through a 3D traversability map, which is a representation of which parts of the 3D space the robot can actually move through. The page itself is mostly a landing card for the paper. It shows a logo, the author list with Shanghai Jiao Tong University as the affiliation, and badges that link out to an arXiv paper, a Bilibili video, and a project website hosted at wuyi2121.github.io/TravExplorer. There are inline images for a system overview diagram, a photo of the robot platform used in the experiments, and a panel showing real-world experiment results. More videos and interactive demonstrations are pointed to on the project page. The README is sparse on purpose at this stage. It states clearly that code will be released upon acceptance of the paper. That means the GitHub repository is reserving the name and acting as a placeholder while the work is under peer review. There is no install section, no usage instructions, no requirements file, and no code in the README excerpt. The citation section also says the citation information will be added once the paper is available. The project is released under the Apache License 2.0, according to the License section, which points readers to a LICENSE file in the repository for details. The language field on GitHub is unset, which is consistent with a repository that does not yet contain source code. Anyone interested in the actual planning algorithm or running it on their own robot will need to wait for the code drop, which the authors tie to paper acceptance.