Spatio-Temporal Metric-Semantic Mapping for Persistent Orchard Monitoring: Method and Dataset
GRASP Laboratory, University of Pennsylvania（GRASP实验室，宾夕法尼亚大学）；Department of Aeronautics and Astronautics, Stanford University（航空航天工程系，斯坦福大学）；Department of Mechanical Engineering, University of California, Merced（机械工程系，加州大学默塞德分校）
Monitoring orchards at the individual tree or fruit level throughout the growth season is crucial for plant phenotyping and horticultural resource optimization, such as chemical use and yield estimation. We present a 4D spatio-temporal metric-semantic mapping system that integrates multi-session measurements to track fruit growth over time. Our approach combines a LiDAR-RGB fusion module for 3D fruit localization with a 4D fruit association method leveraging positional, visual, and topology information for improved data association precision. Evaluated on real orchard data, our method achieves a 96.9% fruit counting accuracy for 1,790 apples across 60 trees, a mean fruit size estimation error of 1.1 cm, and a 23.7% improvement in 4D data association precision over baselines. We publicly release a multimodal dataset covering five fruit species across their growth seasons at https://4d-metric-semantic-mapping.org/
RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics
Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；YXGN Robotics（YXGN机器人）；China University of Mining and Technology（中国矿业大学）；Shenzhen Technology University（深圳技术大学）；Huazhong University of Science and Technology（华中科技大学）；Xiamen University（厦门大学）
GESTO: Human-Centric Spatio-Temporal Memory for Reasoning in Dynamic Scenes
KTH Royal Institute of Technology（瑞典皇家理工学院）；University of Stuttgart（斯图加特大学）；Max Planck Research School for Intelligent Systems（马克斯·普朗克智能系统研究所）；Google Research（谷歌研究院）；TU Munich（慕尼黑工业大学）
R4: Retrieval-Augmented Reasoning for Vision-Language Models in 4D Spatio-Temporal Space
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Esslingen University of Applied Sciences（埃斯林根应用科学大学）；Dr. Ing. h.c. F. Porsche AG（德意志联邦汽车工业协会）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
Spatio-Temporal Reconnection for Multi-Robot Networks using Adaptive Prescribed-Time CBFs
Department of Computer Science, University of Illinois Chicago（伊利诺伊大学芝加哥分校计算机科学系）；Department of Computer Science, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校计算机科学系）
STEGNav: Spatio-Temporal Event Graph Reasoning for Multimodal Lifelong Object Navigation
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）
Multi-robot Learning-based Informative Path Planning Using Spatio-Temporal Gaussian Process Kalman Filter
Carnegie Mellon University（卡内基梅隆大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）
Resolving Spatio-Temporal Entanglement in Video Prediction via Multi-Modal Attention
Indian Institute of Technology (BHU), Varanasi（印度理工学院（巴纳拉斯印度教大学），瓦拉纳西）；University of Colorado, Boulder（科罗拉多大学博尔德分校）；Erasmus+, Intelligent Field Robotic Systems (IFRoS), University of Girona（伊拉斯谟+，智能现场机器人系统（IFRoS），赫罗纳大学）
