Navigating cluttered environments while carrying objects remains a challenging problem for quadruped robots. Compared with works that focused on load-aware locomotion adaptation to maintain gait stability, existing autonomous navigation systems generally ignore the influence of carried objects' geometries and use fixed footprint inflation, leading to overly conservative navigation. To fill this knowledge gap, we propose a two-phase load-aware navigation learning framework. In Phase 1, we train LOADNav, a load-aware navigation policy via reinforcement learning, enabling the robot to adapt its trajectory and clearance behavior according to the geometric size of the carried object. In Phase 2, we train CoGE, an online geometry predictor via imitation learning, which estimates unknown payload geometry from occasional contact events. We evaluate the framework in cluttered environments with loads of varying sizes and shapes. The results show that our navigation policy exhibits clear load-aware behaviors, and the online geometry predictor estimates object geometry from occasional contact events, refining its estimate and adapting the navigation behavior accordingly. Without requiring any prior knowledge of the payload, it narrows the gap to geometry-aware policies.
TRAIN: Terrain-Aware Interactive Navigation for Quadruped Robots via Reinforcement Learning
Department of Robotics, Graduate School of Engineering, Tohoku University（东邦大学机器人学系）；Laboratory for Intelligent Decision and Autonomous Robots, Woodruff School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院智能决策与自主机器人实验室）
Vision-based Navigation for a Small-scale Quadruped Robot Pegasus-Mini
Institute of Robotics and Intelligent Manufacturing (IRIM), The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）机器人与智能制造研究院（IRIM））；Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）深圳人工智能与机器人研究院（AIRS））
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
Georgia Institute of Technology（佐治亚理工学院）；School of Mechanical Engineering, Georgia Institute of Technology（佐治亚理工学院机械工程学院）；SkyMul Inc（SkyMul公司）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
Robust Localization, Mapping, and Navigation for Quadruped Robots
École Centrale de Nantes（法国南特中央理工大学）；Technical University of Darmstadt（德累斯顿技术大学）；Poznan University of Technology（波兹南理工大学）；IDEAS NCBR；German Research Center for AI (DFKI)（德国人工智能研究中心）；Università degli Studi di Milano（米兰大学）
Load-bearing Assessment for Safe Locomotion of Quadruped Robots on Collapsing Terrain
Dynamic Legged Systems Lab, Istituto Italiano di Tecnologia (IIT)（动态腿系统实验室，意大利技术研究院）；Department of Mechanical Engineering, São Carlos School of Engineering (EESC), University of São Paulo (USP)（机械工程系，圣卡洛斯工程学院（EESC），圣保罗大学）
Autonomous Navigation of Quadrupeds Using Coverage Path Planning with Morphological Skeleton Map
Department of Cognitive Robotics, ME, Delft University of Technology（认知机器人系，代尔夫特理工大学）；Department of Microelectronics, EEMCS, Delft University of Technology（微电子系，代尔夫特理工大学）
Autonomous Grasping On Quadruped Robot With Task Level Interaction
Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia（电气工程系，十月份十号技术大学，Surabaya，印度尼西亚）；Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia（计算机工程系，十月份十号技术大学，Surabaya，印度尼西亚）；Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia（信息系，十月份十号技术大学，Surabaya，印度尼西亚）
TONAV: Task-Oriented Navigation and Action-Velocity Chunk Learning for Articulated Object Quadrupedal Mobile Manipulation
School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；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）；College of Semiconductors (College of Integrated Circuits), Hunan University（湖南大学半导体学院（集成电路学院））
Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach
Independent Researcher（独立研究者）；Department of Construction Management, Western New England University（西雅图新英格兰大学建设管理系）
