Activity recognition is very useful in scenarios where robots interact with, monitor or assist humans. In the past years many types of activities -- single actions, two persons interactions or ego-centric activities, to name a few -- have been analyzed. Whereas traditional methods treat such types of activities separately, an autonomous robot should be able to detect and recognize multiple types of activities to effectively fulfill its tasks. We propose a method that is intrinsically able to detect and recognize activities of different types that happen in sequence or concurrently. We present a new unified descriptor, called Relation History Image (RHI), which can be extracted from all the activity types we are interested in. We then formulate an optimization procedure to detect and recognize activities of different types. We apply our approach to a new dataset recorded from a robot-centric perspective and systematically evaluate its quality compared to multiple baselines. Finally, we show the efficacy of the RHI descriptor on publicly available datasets performing extensive comparisons.
Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets
University of California, San Diego（加利福尼亚大学圣迭戈分校）；Tongji University（同济大学）；Shanghai Jiao Tong University（上海交通大学）；University of Maryland, College Park（马里兰大学帕克分校）；Tsinghua University（清华大学）
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
UC Berkeley（加州大学伯克利分校）；ICSI（国际计算机科学研究所）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Saarland University（萨尔大学）；Technische Universität München（慕尼黑工业大学）；Stanford University（斯坦福大学）；SPIEGEL-Verlag（明镜出版社）
RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World
Waseda University（早稻田大学）；Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong（香港中文大学）；MEGVII Technology（旷视科技）；Chinese Academy of Sciences（中国科学院）
Challenges in Multi-centric Generalization: Phase and Step Recognition in Roux-en-Y Gastric Bypass Surgery
University of Basel（巴塞尔大学）；University of Strasbourg（斯特拉斯堡大学）；University of Verona（维罗纳大学）；University Hospital of Strasbourg（斯特拉斯堡大学医院）；Inselspital Bern University Hospital（伯尔尼大学医院）；IRCAD France（法国消化外科和腹腔镜影像研究所）
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；IIIS, Tsinghua University（清华大学人工智能研究院）；Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波工程技术院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；ByteDance（字节跳动）；Kling, Kuaishou Technology（Kling，快手科技）；GigaAI（极佳科技）；AIR, Tsinghua University（清华大学人工智能研究院）
Object-Centric Action-Enhanced Representations for Robot Visuo-Motor Policy Learning
School of ECE, National Technical University of Athens（希腊国家技术大学电子工程学院）；Robotics Institute, Athena Research Center（阿提卡研究中心机器人研究所）；School of ECE, NTUA（希腊国家技术大学电子工程学院）；HERON - Hellenic Robotics Center of Excellence（希腊机器人卓越中心）
RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；University of Science and Technology of China（中国科学技术大学）
A Distributed Multi-Modal Sensing Approach for Human Activity Recognition in Real-Time Human-Robot Collaboration
University of Genoa（热那亚大学）；The University of Danang–University of Science and Technology（丹江-科学技术大学）；Japan Advanced Institute of Science and Technology（日本先进科学研究院）
FindAnything: Open-Vocabulary and Object-Centric Mapping for Robot Exploration in Any Environment
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；ETH Zurich（苏黎世联邦理工学院）；Department of Computing（计算学院）；Imperial College London（伦敦帝国理工学院）；University of Patras（帕特拉大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
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）
