Large Language Models (LLMs) have recently empowered agentic frameworks to exhibit advanced reasoning and planning capabilities. However, their integration in robotic control pipelines remains limited in two aspects: (1) prior \ac{llm}-based approaches often lack modular, agentic execution mechanisms, limiting their ability to plan, reflect on outcomes, and revise actions in a closed-loop manner; and (2) existing benchmarks for manipulation tasks focus on low-level control and do not systematically evaluate multistep reasoning and linguistic variation. In this paper, we propose Agentic LLM for Robot Manipulation (ALRM), an LLM-driven agentic framework for robotic manipulation. ALRM integrates policy generation with agentic execution through a ReAct-style reasoning loop, supporting two complementary modes: Code-asPolicy (CaP) for direct executable control code generation, and Tool-as-Policy (TaP) for iterative planning and tool-based action execution. To enable systematic evaluation, we also introduce a novel simulation benchmark comprising 56 tasks across multiple environments, capturing linguistically diverse instructions. Experiments with ten LLMs demonstrate that ALRM provides a scalable, interpretable, and modular approach for bridging natural language reasoning with reliable robotic execution. Results reveal Claude-4.1-Opus as the top closed-source model and Falcon-H1-7B as the top open-source model under CaP.
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）
Cracking Mobile Manipulation: From Failure Diagnosis to Agentic Robot System
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；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）；Alibaba DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）；University of Technology Sydney（悉尼科技大学）；University of Liverpool（利物浦大学）
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）
OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Shanghai AI Laboratory（上海人工智能实验室）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Shanghai Jiaotong University（上海交通大学）；Tsinghua University（清华大学）
A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
Sun Yat-sen University（中山大学）；Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China（教育部机器智能与先进计算重点实验室）；Imperial College London（帝国理工学院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Shenzhen Loop Area Institute（深圳河套学院）
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））
DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation
University of British Columbia（不列颠哥伦比亚大学）；National University of Singapore（新加坡国立大学）；University of Cambridge（剑桥大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning
Department of Computer Science, Tsinghua University（清华大学计算机科学系）；National College for Excellent Engineers, Beihang University（北京航空航天大学卓越工程师学院）；Wuxi Dexteroushands Robotic Technology Co.（无锡灵犀机器人技术有限公司）
VLA^2: Empowering Vision-Language-Action Models with an Agentic Framework for Unseen Concept Manipulation
Zhejiang University（浙江大学）；MiLAB, Westlake University（西湖大学 MiLAB）；Southern University of Science and Technology（南方科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
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。
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）
SUN: Agentic Robot Policy Learning with Persistent Task Programs
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；Amazon（亚马逊公司）；Robotics and AI Institute（机器人与人工智能研究院）
