Towards Agentic Robots: Model, Data, Environment, and Harness
MMLab, The Chinese University of Hong Kong（香港中文大学MMLab）；Fudan University（复旦大学）；NovaXBot；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Sun Yat-sen University（中山大学）
Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human instructions, interact with robotic environments, rectify execution errors autonomously, and dynamically adjust operational strategies. Although this field develops fast, current studies are not well connected. Researchers have no consistent view of how to combine different agentic functions into a complete robot system. This paper provides a unified overview of existing research on agentic robots. We first trace the development from robot policies to agentic systems, covering vision-language-action models, world models, and world-action models. We then clarify the definition and scope of agentic robots and organize existing research into seventeen categories across four functional domains: Model, Data, Environment, and Harness. Across these domains, we examine how agents shape model learning and robot decisions, collect and curate robot data, construct worlds, tasks, and tests for learning and evaluation, and organize capabilities for robot use. Finally, we discuss open challenges and outline directions for future research. Looking forward, we hope this work contributes to a clearer understanding of agentic robots and supports new approaches to making them more capable, reliable, and adaptive.
Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents
Jilin University（吉林大学）；Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；Huazhong University of Science and Technology（华中科技大学）；Southern University of Science and Technology（南方科技大学）；Lehigh University（莱斯大学）；Shanghai Jiao Tong University（上海交通大学）
本文提出脑启发框架 Agentic Robot，利用 SAP 协调推理、VLA 执行与时序验证形成闭环，在 LIBERO 长程操控任务上达到 79.6% 的最先进成功率。
Towards Embodied Agentic AI: Review and Classification of LLM- and VLM-Driven Robot Autonomy and Interaction
Department of Computing, University of Turku（图尔库大学计算机系）；Institute of Computer Science, Zurich University of Applied Sciences（应用科学大学计算机科学研究所）；Centre for Artificial Ingelligence, Zurich University of Applied Sciences（应用科学大学人工智能中心）；Agentic Systems Lab, Department of Management, Technology and Economics, ETH Zürich（苏黎世联邦理工学院管理、科技与经济系代理系统实验室）；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙技术大学数学与信息科学学院）
RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
AgiBot；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）；MoE Key Lab of Artificial Intelligence, AI Institute, SJTU（上海交通大学人工智能研究院教育部人工智能重点实验室）
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
TU Darmstadt（达姆施塔特工业大学）；Honda Research Institute Europe（本田欧洲研究所）；Columbia University（哥伦比亚大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；University of Würzburg（维尔茨堡大学）；Hessian.AI（黑森人工智能中心）
Learning Robust Execution in Robotic Manipulation with Agentic Reinforcement Learning
School of Inteligence Science and Engineering, the Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳）智能科学与工程学院）；Faculty of Robot Science and Engineering, Northeastern University（东北大学机器人科学与工程学院）
Agentic AI for Robot Control: Flexible but still Fragile
ETH Zurich（苏黎世联邦理工学院）；University of Cambridge（剑桥大学）；Technical University of Munich（慕尼黑技术大学）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）；German Aerospace Center (DLR)（德国航空航天中心（DLR））
UniManip: General-Purpose Zero-Shot Robotic Manipulation with Agentic Operational Graph
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；Department of Automation, Tsinghua University, China（清华大学自动化系）
RegenHarness: A Robot Agent Harness with Evidence-Gated Recursive Self-Improvement
AI Lab, Country Garden Services Group（碧桂园服务集团AI实验室）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）；Omni AI
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
Peking University（北京大学）；Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）；Tsinghua University（清华大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Test-Driven Agentic Framework for Reliable Robot Controller
Department of Electrical and Computer Engineering（电气与计算机工程系）；University of California, Riverside（加州大学河滨分校）；Department of Mathematics（数学系）；Department of Computer Science（计算机科学系）；University of Colorado, Boulder（科罗拉多大学波德分校）
