CausalNav: A Long-term Embodied Navigation System for Autonomous Mobile Robots in Dynamic Outdoor Scenarios
Center for Artificial Intelligence and Robotics, Shenzhen International Graduate School, Tsinghua University（人工智能与机器人中心，深圳国际研究生院，清华大学）；Peng Cheng Laboratory（鹏城实验室）
Hongbo Duan, Shangyi Luo, Zhiyuan Deng, Yanbo Chen, Yuanhao Chiang, Yi Liu, Fangming Liu, Xueqian Wang
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Accepted by IEEE Robotics and Automation Letters (RA-L)
Autonomous language-guided navigation in large-scale outdoor environments remains a key challenge in mobile robotics, due to difficulties in semantic reasoning, dynamic conditions, and long-term stability. We propose CausalNav, the first scene graph-based semantic navigation framework tailored for dynamic outdoor environments. We construct a multi-level semantic scene graph using LLMs, referred to as the Embodied Graph, that hierarchically integrates coarse-grained map data with fine-grained object entities. The constructed graph serves as a retrievable knowledge base for Retrieval-Augmented Generation (RAG), enabling semantic navigation and long-range planning under open-vocabulary queries. By fusing real-time perception with offline map data, the Embodied Graph supports robust navigation across varying spatial granularities in dynamic outdoor environments. Dynamic objects are explicitly handled in both the scene graph construction and hierarchical planning modules. The Embodied Graph is continuously updated within a temporal window to reflect environmental changes and support real-time semantic navigation. Extensive experiments in both simulation and real-world settings demonstrate superior robustness and efficiency.
If robots are to become ubiquitous, they will need to be able to adapt to complex and dynamic environments. Robots that can adapt their bodies while deployed might be flexible and robust enough to meet this challenge. Previous work on dynamic robot morphology has focused on simulation, combining simple modules, or switching between locomotion modes. Here, we present an alternative approach: a self-reconfigurable morphology that allows a single four-legged robot to actively adapt the length of its legs to different environments. We report the design of our robot, as well as the results of a stu…
NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
GeoNav: Empowering MLLMs with dual-scale geospatial reasoning for language-goal aerial navigation
College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；BNRist, Tsinghua University（清华大学BNRist）
TPT-Bench: A Large-Scale, Long-Term and Robot-Egocentric Dataset for Benchmarking Target Person Tracking
Robotics and Computer Vision Laboratory, Southern University of Science and Technology, Shenzhen, China（机器人与计算机视觉实验室，南方科技大学，深圳，中国）；Human-Robot Interfaces and Interaction Laboratory, Italian Institute of Technology, Geona, Italy（人机交互实验室，意大利理工学院，Geona，意大利）；Robotics Perception and Intelligence Laboratory, Southern University of Science and Technology, Shenzhen, China（机器人感知与智能实验室，南方科技大学，深圳，中国）；Chen Kar-Shun Robotics Institute, Hong Kong University of Science and Technology, Hong Kong SAR, China（陈卡尔顺机器人研究所，香港科技大学，香港特别行政区，中国）
AutoFly: Vision-Language-Action Model for UAV Autonomous Navigation in the Wild
Southeast University（东南大学）；Purple Mountain Labs（紫金山实验室）；The Chinese University of Hong Kong（香港中文大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）
For embodied agents, navigation is an important ability but not an isolated goal. Agents are also expected to perform specific tasks after reaching the target location, such as picking up objects and assembling them into a particular arrangement. We combine Vision-and-Language Navigation, assembling of collected objects, and object referring expression comprehension, to create a novel joint navigation-and-assembly task, named ArraMon. During this task, the agent (similar to a PokeMON GO player) is asked to find and collect different target objects one-by-one by navigating based on natural lang…
Beijing Innovation Center of Humanoid Robotics（北京人形机器人创新中心）；Beijing Institute of Technology（北京理工大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
UniTrackPLA: Unified Panorama-Language-Action Model for Instruction-Guided Navigation and Dynamic Person Tracking
College of Integrated Circuits, Hunan University（湖南大学集成电路学院）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（湖南大学机器人视觉感知与控制技术国家工程研究中心）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学智能工程学院）；Suzhou VSDeep Intelligent Technology Co., Ltd.（苏州维深智能科技有限公司）
