In this paper, we propose a generalizable knowledge framework for data abstraction, i.e. finding compact abstract model for input data using predefined abstract terms. Based on these abstract terms, intelligent autonomous systems, such as a robot, should be able to make inference according to specific knowledge base, so that they can better handle the complexity and uncertainty of the real world. We propose to realize this framework by combining Markov logic networks (MLNs) and data driven MCMC sampling, because the former are a powerful tool for modelling uncertain knowledge and the latter provides an efficient way to draw samples from unknown complex distributions. Furthermore, we show in detail how to adapt this framework to a certain task, in particular, semantic robot mapping. Based on MLNs, we formulate task-specific context knowledge as descriptive soft rules. Experiments on real world data and simulated data confirm the usefulness of our framework.
Anyview: Generalizable Indoor 3D Object Detection with Variable Frames
School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications（智能工程与自动化学院，北京邮电大学）；Department of Automation, Tsinghua University（自动化系，清华大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）
Distilling LLM Prior to Flow Model for Generalizable Agent's Imagination in Object Goal Navigation
Sun Yat-sen University（中山大学）；The University of Hong Kong（香港大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education（教育部机器智能与高级计算重点实验室）
SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining
University of Amsterdam（阿姆斯特丹大学）；Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）；INSAIT, Sofia University ”St. Kliment Ohridski”（索菲亚大学”圣克莱门特·欧赫里迪斯”研究所）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Pisa（比萨大学）；University of Trento（特伦特大学）
Implicit Geometry Representations for Vision-and-Language Navigation from Web Videos
Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence（计算机视觉系，Mohamed Bin Zayed人工智能大学）；School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）；Shenzhen Campus of Sun Yat-Sen University（中山大学深圳校区）
Learning to Drive Anywhere with Model-Based Reannotation
Department of Electrical Engineering and Computer Sciences , University of California, Berkeley, CA USA（电气工程与计算机科学系，加州大学伯克利分校）；Toyota Motor North America, Inc（丰田北美公司）；Department of Electrical and Computer Engineering, Princeton University（电气与计算机工程系，普林斯顿大学）
