End-to-end autonomous driving models increasingly benefit from large vision-language models for semantic understanding, yet safe and reliable planning under long-tail conditions remains challenging, particularly in mixed-traffic environments involving heterogeneous road users and rare safety-critical interactions. This paper proposes HERMES, a holistic risk-aware end-to-end multimodal driving framework that explicitly incorporates long-tail semantic knowledge into trajectory planning. HERMES employs a foundation-model-assisted annotation pipeline to construct structured Long-Tail Scene Context and Long-Tail Planning Context, capturing hazard-centric scene information, maneuver intent, and risk-aware planning guidance. A Tri-Modal Driving Module then integrates multi-view visual observations, historical ego-motion, and long-tail semantic instructions through intent- and risk-aware conditioning for trajectory generation. Extensive experiments on a large-scale real-world long-tail driving benchmark demonstrate consistent improvements over representative recent baselines in overall planning performance and across diverse safety-critical scenarios. Ablation studies further validate the effectiveness and complementary roles of the major components within HERMES.
EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI
The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
EHC-MM: Embodied Holistic Control for Mobile Manipulation
Beijing Samsung Telecom R&D Center（北京三星通信技术研发中心）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；PKU-WUHAN Institute for Artificial Intelligence（北京大学-武汉人工智能研究院）；Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）
PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·扎耶德人工智能大学）；Tsinghua Shenzhen International Graduate School, Tsinghua University, China（清华大学深圳国际研究生院）；Shenzhen Campus of Sun Yat-sen University（孙中山大学深圳校区）；Spatialtemporal AI（时空人工智能）；Shanghai Jiao Tong University（上海交通大学）
IndustryNav: Evaluating Spatial Reasoning and Safety of Embodied Agents in Dynamic Industrial Navigation
Michigan State University（密歇根州立大学）；Independent Researcher（独立研究者）；University of California, Irvine（加州大学尔湾分校）；Ohio State University（俄亥俄州立大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Pittsburgh（匹兹堡大学）；University of Virginia（弗吉尼亚大学）；Arizona State University（亚利桑那州立大学）；Purdue University Northwest（普渡大学西北分校）
TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models
The University of Hong Kong（香港大学）；Peking University（北京大学）；OpenGVLab（开放智能机器研究中心）；Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）
MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations
Shanghai AI Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Zhiyuan College, Shanghai Jiao Tong University（上海交通大学紫阳学院）；CPII under InnoHK（创新香港下的CPII）
Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models
Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）；Wangxuan Institute of Computer Technology, Peking University（北京大学王轩计算机技术研究所）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）
RoboTidy : A 3D Gaussian Splatting Household Tidying Benchmark for Embodied Navigation and Action
Huazhong University of Science and Technology（华中科技大学）；The University of Hong Kong（香港大学）；INFIFORCE Intelligent Technology Co., Ltd.（INFIFORCE智能技术有限公司）；Zhejiang University（浙江大学）；Guangming Lab, Shenzhen（深圳光明实验室）
Vision-Based Tactile Intelligence for Robotics: Sensing, Learning, and Embodied Manipulation
Great Bay University（大湾区大学）；Tsinghua University（清华大学）；The University of Hong Kong（香港大学）；Nanyang Technological University（南洋理工大学）；The Hong Kong Polytechnic University（香港理工大学）；South China University of Technology（华南理工大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；King’s College London（伦敦国王学院）
Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation
AIRI；Moscow Institute of Physics and Technology (MIPT)（莫斯科物理技术学院）；Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Sberbank, Robotics Center（Sberbank机器人中心）；Skoltech
本文提出 Kitchen-R 基准，在 Isaac Sim 数字孪生厨房环境中统一评估任务规划与底层控制，并提供视觉语言模型规划、扩散策略控制及轨迹采集系统，支持独立与集成评估，弥补具身 AI 中语言引导机器人整体评测的空白。
