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arXiv 2604.04664cs.ROcs.AIcs.MA
ROSClaw：一种用于异构多智能体协作的分层语义-物理框架
ROSClaw: A Hierarchical Semantic-Physical Framework for Heterogeneous Multi-Agent Collaboration
Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；National Key Laboratory of Autonomous Intelligent Unmanned Systems (Tongji University)（自主智能无人系统全国重点实验室（同济大学））；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）；Tongji University（同济大学）；College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）
Rongfeng Zhao, Xuanhao Zhang, Zhaochen Guo, Xiang Shao, Zhongpan Zhu, Bin He, Jie Chen
The integration of large language models (LLMs) with embodied agents has improved high-level reasoning capabilities; however, a critical gap remains between semantic understanding and physical execution. While vision-language-action (VLA) and vision-language-navigation (VLN) systems enable robots to perform manipulation and navigation tasks from natural language instructions, they still struggle with long-horizon sequential and temporally structured tasks. Existing frameworks typically adopt modular pipelines for data collection, skill training, and policy deployment, resulting in high costs in experimental validation and policy optimization. To address these limitations, we propose ROSClaw, an agent framework for heterogeneous robots that integrates policy learning and task execution within a unified vision-language model (VLM) controller. The framework leverages e-URDF representations of heterogeneous robots as physical constraints to construct a sim-to-real topological mapping, enabling real-time access to the physical states of both simulated and real-world agents. We further incorporate a data collection and state accumulation mechanism that stores robot states, multimodal observations, and execution trajectories during real-world execution, enabling subsequent iterative policy optimization. During deployment, a unified agent maintains semantic continuity between reasoning and execution, and dynamically assigns task-specific control to different agents, thereby improving robustness in multi-policy execution. By establishing an autonomous closed-loop framework, ROSClaw minimizes the reliance on robot-specific development workflows. The framework supports hardware-level validation, automated generation of SDK-level control programs, and tool-based execution, enabling rapid cross-platform transfer and continual improvement of robotic skills. Ours project page: https://www.rosclaw.io/.
HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams
Cognitive Science Department（认知科学系）；Department of Computer Science（计算机科学系）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
One of the key challenges in the collaboration within heterogeneous multi-robot systems is the optimization of the amount and type of data to be shared between robots with different sensing capabilities and computational resources. In this paper, we present a novel approach to managing collaboration terms in heterogeneous multi-robot systems with blockchain technology. Leveraging the extensive research of consensus algorithms in the blockchain domain, we exploit key technologies in this field to be integrated for consensus in robotic systems. We propose the utilization of proof of work systems…
COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models
Northwestern Polytechnical University（西北工业大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Institute of Artificial Intelligence, China Telecom Corp Ltd（中国电信集团有限公司人工智能研究院）
In recent years, multi-robot systems have received increasing attention from both industry and academia. Besides the need of accurate and robust estimation of relative localization, security and trust in the system are essential to enable wider adoption. In this paper, we propose a framework using Hyperledger Fabric for multi-robot collaboration in industrial applications. We rely on blockchain identities for the interaction of ground and aerial robots, and use smart contracts for collaborative decision making. The use of ultra-wideband (UWB) localization for both autonomous navigation and rob…
Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence
Dept. of Electrical and Computer Engineering at Texas A&M University（德克萨斯A&M大学电气与计算机工程系）；GRASP Laboratory at the University of Pennsylvania（宾夕法尼亚大学GRASP实验室）
MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models
Institute of Advanced Technology, University of Science and Technology of China (USTC)（中国科学技术大学先进技术研究院）；School of Artificial Intelligence and Data Science, USTC（中国科学技术大学人工智能与数据科学学院）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；School of Computer Science and Technology, USTC（中国科学技术大学计算机科学与技术学院）
Alexa AI, Amazon Inc（亚马逊公司Alexa人工智能部门）；University of Southern California（南加州大学）
We propose a multimodal (vision-and-language) benchmark for cooperative and heterogeneous multi-agent learning. We introduce a benchmark multimodal dataset with tasks involving collaboration between multiple simulated heterogeneous robots in a rich multi-room home environment. We provide an integrated learning framework, multimodal implementations of state-of-the-art multi-agent reinforcement learning techniques, and a consistent evaluation protocol. Our experiments investigate the impact of different modalities on multi-agent learning performance. We also introduce a simple message passing me…
NeuroMesh: A Unified Neural Inference Framework for Decentralized Multi-Robot Collaboration
New York University（纽约大学）；U.S. Army Combat Capabilities Development Command, Army Research Laboratory（美国陆军作战能力发展司令部，陆军研究实验室）；Vanderbilt University（范德比尔特大学）；University of California Berkeley, Department of Electrical Engineering and Computer Sciences（加州大学伯克利分校，电气工程与计算机科学系）
We Choose to Go to Space: Agent-driven Human and Multi-Robot Collaboration in Microgravity
Beijing University of Posts and Telecommunications（北京邮电大学）；TongJi University（同济大学）；Beihang University（北京航空航天大学）；Beijing Normal University（北京师范大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Carnegie Mellon University（卡内基梅隆大学）
HeRo Lab, Department of Computer Science, University of Georgia（佐治亚大学计算机科学系HeRo实验室）；University of Georgia（佐治亚大学）
This paper focuses on the teaming aspects and the role of heterogeneity in a multi-robot system applied to robot-aided urban search and rescue (USAR) missions. We propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the system's performance in terms of the group utility and energy cost to achieve the rescue mission in a limited time. From the theoretical analysis, we prove that the needs-driven cooperation in a heterogeneous robot system enables higher group utility than a homogeneous robot system. We also perform simulation experi…
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（1 多媒体信息处理国家重点实验室，计算机学院，北京大学）；Beijing Academy of Artificial Intelligence（2 北京人工智能研究院）；Institute of Automation, Chinese Academy of Sciences（3 中国科学院自动化研究所）；Beihang University（4 北航大学）
The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust scheduling in multi-agent systems. Existing approaches, from vision-language-action (VLA) models to hierarchical frameworks, fall short due to their reliance on limited or dividual-agent memory. This fundamentally constrains their ability to learn over long horizons, scale to heterogeneous teams, or recover from failures, highlighting the need for a unified memory representation. To address these limitations, we introduc…
Melding LLM and temporal logic for reliable human-swarm collaboration in complex scenarios
School of Advanced Manufacturing and Robotics, Peking University（北京大学先进制造与机器人学院）；Division of Natural and Applied Sciences, Duke Kunshan University（杜克昆山大学自然与应用科学学院）；School of Automaton, Southeast University（东南大学自动化学院）；School of Automation Science and Electrical Engineering, Beihang University（北航自动化科学与电气工程学院）
University of Pennsylvania（宾夕法尼亚大学）；NASA Ames Intelligent Robotics Group（美国国家航空航天局艾姆斯智能机器人研究组）
In this work, we present an end-to-end heterogeneous multi-robot system framework where ground robots are able to localize, plan, and navigate in a semantic map created in real time by a high-altitude quadrotor. The ground robots choose and deconflict their targets independently, without any external intervention. Moreover, they perform cross-view localization by matching their local maps with the overhead map using semantics. The communication backbone is opportunistic and distributed, allowing the entire system to operate with no external infrastructure aside from GPS for the quadrotor. We e…
This conceptual paper overviews how blockchain technology is involving the operation of multi-robot collaboration for combating COVID-19 and future pandemics. Robots are a promising technology for providing many tasks such as spraying, disinfection, cleaning, treating, detecting high body temperature/mask absence, and delivering goods and medical supplies experiencing an epidemic COVID-19. For combating COVID-19, many heterogeneous and homogenous robots are required to perform different tasks for supporting different purposes in the quarantine area. Controlling and decentralizing multi-robot p…
Ground robots always get collision in that only if they get close to the obstacles, can they sense the danger and take actions, which is usually too late to avoid the crash, causing severe damage to the robots. To address this issue, we present collaboration of aerial and ground robots in recognition of feasible region. Taking the aerial robots' advantages of having large scale variance of view points of the same route which the ground robots is on, the collaboration work provides global information of road segmentation for the ground robot, thus enabling it to obtain feasible region and adjus…
University of Genoa（热那亚大学）；Istituto Italiano di Tecnologia（意大利理工学院）
The presence and coexistence of human operators and collaborative robots in shop-floor environments raises the need for assigning tasks to either operators or robots, or both. Depending on task characteristics, operator capabilities and the involved robot functionalities, it is of the utmost importance to design strategies allowing for the concurrent and/or sequential allocation of tasks related to object manipulation and assembly. In this paper, we extend the \textsc{FlexHRC} framework presented in \cite{darvish2018flexible} to allow a human operator to interact with multiple, heterogeneous r…
Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments
College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）人工智能学院）；Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程系）
Ultra-wideband technology has emerged in recent years as a robust solution for localization in GNSS denied environments. In particular, its high accuracy when compared to other wireless localization solutions is enabling a wider range of collaborative and multi-robot application scenarios, being able to replace more complex and expensive motion-capture areas for use cases where accuracy in the order of tens of centimeters is sufficient. We present the first survey of UWB-based localization focused on multi-UAV systems and heterogeneous multi-robot systems. We have found that previous literatur…
Transforming Monolithic Foundation Models into Embodied Multi-Agent Architectures for Human-Robot Collaboration
Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）
University of Turku（图尔库大学）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
Effective collaboration in multi-robot systems requires accurate and robust estimation of relative localization: from cooperative manipulation to collaborative sensing, and including cooperative exploration or cooperative transportation. This paper introduces a novel approach to collaborative localization for dense scene reconstruction in heterogeneous multi-robot systems comprising ground robots and micro-aerial vehicles (MAVs). We solve the problem of full relative pose estimation without sliding time windows by relying on UWB-based ranging and Visual Inertial Odometry (VIO)-based egomotion …
