Vision-Language Models (VLMs) have shown strong visual understanding and are increasingly deployed in embodied AI systems, where reliable perception under real conditions is essential. However, existing benchmarks assess VLMs using clean images or isolated perturbations rather than stresses caused by physical scene formation. This design has two limitations: it covers only a narrow subset of everyday visual stresses, and some perturbations rarely appear in realistic embodied scenes. This gap raises a fundamental question: how can we define visual stress in a principled way that captures the diverse factors encountered in physical environments? To address this question, we formulate visual perception from an inverse graphics perspective and introduce RoboStressBench, a benchmark for evaluating VLM robustness to physical visual stress in embodied scenes. Inspired by the physical rendering equation, RoboStressBench decomposes visual stress into four physically grounded dimensions: Material (M), Viewpoint (V), Lighting (L), and Geometry (G). This design enables RoboStressBench to cover a broad range of visual stresses in real-world environments, while allowing controlled analysis of their effects on VLM capabilities such as visual recognition, reasoning, and planning. Through comprehensive evaluations of state-of-the-art VLMs, we identify stress-specific failure modes and reveal that different physical factors degrade different embodied capabilities, which are often obscured by aggregate accuracy. We further introduce a stress-aware agentic solver that detects visual stressors and invokes visual-editing skills before reasoning, improving robustness in high-stress scenarios. Overall, RoboStressBench provides a principled evaluation framework for diagnosing and improving VLM perception under real-world physical stress, supporting the development of more reliable embodied AI systems.
Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；Nanjing University（南京大学）；Fudan University（复旦大学）；Tsinghua University（清华大学）；NUS（新加坡国立大学）；Northeastern University（东北大学）；Shenzhen University（深圳大学）
EPIC-Bench: A Perception-Centric Benchmark for Fine-Grained Embodied Visual Grounding in Vision-Language Models
X-Humanoid；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；University of Manchester（曼彻斯特大学）；Monash University（墨尔本大学）；Celonis AI；University of New South Wales（新南威尔士大学）
VLM Can Be a Good Assistant: Enhancing Embodied Visual Tracking with Self-Improving Vision-Language Models
State Key Laboratory of Complex & Critical Software Environment, Beihang University（北京航空航天大学复杂与关键软件环境国家重点实验室）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；City University of Macau（澳门城市大学）；Center on Frontiers of Computing Studies, School of Computer Science, Nat’l Eng. Research Center of Visual Technology, Peking University（北京大学计算机学院前沿计算研究中心、国家工程视觉技术研究中心）
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
Southern University of Science and Technology（南方科技大学）；JD Explore Academy（京东探索研究院）；University of Technology Sydney（悉尼科技大学）；University of Maryland, College Park（马里兰大学学院公园分校）；Yunnan University（云南大学）
EmbodiedMidtrain: Bridging the Gap between Vision-Language Models and Vision-Language-Action Models via Mid-training
Language Technologies Institute, Carnegie Mellon University（卡内基梅隆大学语言技术研究所）；Bosch Research North America & Bosch Center for Artificial Intelligence (BCAI)（博世北美研究部及博世人工智能中心（BCAI））
Embodied4C: Measuring What Matters for Embodied Vision-Language Navigation
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；UAS Esslingen（埃森嫩大学）；TU Wien（维也纳技术大学）；Dr. Ing. h.c. F. Porsche AG（保时捷股份有限公司）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
SpatialBot: Precise Spatial Understanding with Vision Language Models
School of Artificial Intelligence, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Stanford University（斯坦福大学）；BAAI（北京智源人工智能研究院）；Peking University（北京大学）；University of Oxford（牛津大学）；Southeast University（东南大学）
Recurrent Reasoning with Vision-Language Models for Estimating Long-Horizon Embodied Task Progress
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学北京校区人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（教育部下一代智能搜索与推荐工程研究中心）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Institute for AI Industry Research (AIR), Tsinghua University（清华大学人工智能产业研究院）
AlloEgo-VLM: Disambiguating Allocentric and Egocentric Reference Frames in Vision-Language Models
National Yang Ming Chiao Tung University（国立阳明交通大学）；Institute of Computer Science and Engineering（工程与计算机科学学院）；College of Artificial Intelligence（人工智能学院）
针对VLMs理解空间语义时 allocentric 与 egocentric 参考框架的歧义问题，构建数据集AlloEgo-View并开发框架AlloEgo-VLM，经NVIDIA Isaac Sim平台验证其在具身机器人开放式物体搜索任务中的有效性。
UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
Institute for Interdisciplinary Information Sciences, Tsinghua University,Beijing, China（信息科学交叉研究所，清华大学，北京，中国）；Shanghai Qi Zhi Institute, Shanghai, China（上海启智研究院，上海，中国）
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
Tsinghua University（清华大学）；Automotive and Robotics, Xiaomi Corporation（小鹏汽车与机器人部，小鹏科技）；National University of Singapore（新加坡国立大学）；McGill University（麦吉尔大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
CLiViS: Unleashing Cognitive Map through Linguistic-Visual Synergy for Embodied Visual Reasoning
School of Computer Science and Technology, East China Normal University（东华大学计算机科学与技术学院）；King Abdullah University of Science and Technology（科廷大学）；Fudan University（复旦大学）
VIKI-R: Coordinating Embodied Multi-Agent Cooperation via Reinforcement Learning
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；University of Oxford（牛津大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
SNOW: Spatio-Temporal Scene Understanding with World Knowledge for Open-World Embodied Reasoning
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联合会）；University of Michigan（密歇根大学）；Voxel51 Inc.（Voxel51公司）
