Multi-Agent Control

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12 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 68

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Learning Spatiotemporal Tubes for Full Class of Signal Temporal Logic Tasks for Control of Unknown Systems under Input Constraints
Jul 8, 2026Ahan Basu, Ratnangshu Das, Soumyodipta Nath +2Signal Temporal Logic Multi-Agent Control
Centre for Cyber-Physical Systems Indian Institute of Science, Bengaluru, India · Electrical Engineering Department, Eindhoven Institute of Technology, Netherlands
Anytime Plug-and-Play Control with Contract-Based Distributed MPC
Jul 5, 2026Sabrina Bodmer, Danilo Saccani, Melanie N. Zeilinger +1Safety-Critical Control Multi-Agent Control
Institute for Dynamic Systems and Control, ETH Zurich, Switzerland · Institute of Mechanical Engineering, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems
Jul 1, 2026Srini Ramaswamy, Wang MiaoshengCyber-Physical Systems Safety-Critical Control
DNRS.ai, USA · Independent Researcher
ATM: CID-Brokered Pre-Write Admission for Multi-Agent Code Co-Synthesis
Jun 29, 2026Eagl HuangMulti-Agent Coordination Software Engineering Agents
Eagl Huang
Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection
Jun 29, 2026Jan Stenner, Hans Harder, Sebastian PeitzSparse Recovery Multi-Agent Control
Universität Paderborn, Pohlweg 51, 33098 Paderborn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Joseph-von-Fraunhofer-Straße 25, 44227 Dortmund, Germany · TU Dortmund, Joseph-von-Fraunhofer-Straße 25, 44227 Dortmund, Germany
Resilient Output Containment under Undisclosed Leader Dynamics and Actuator Attacks
Jun 25, 2026Mohammadreza Nematollahi, Khashayar Khorasani, Nader MeskinCyber-Physical Systems Multi-Agent Control
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada. · Department of Electrical Engineering, Qatar University, Doha, Qatar.
IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
Jun 25, 2026Chenlong Liu, Zhuohui Zhang, Xinyan Chen +3Sim-to-Real Transfer Multi-Agent Control
College of Electronic and Information Engineering, Tongji University, Shanghai, China. · Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China.
Causality-Based Parametric Control Barrier Function for Safe Multi-Vehicle Interaction
Jun 23, 2026Yiwei Lyu, Caleb Chang, John M. DolanCollision Avoidance Safety-Critical Control
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77845 USA · School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, 15213 USA
Welfarist Control Design -- How to fulfill the societal mandate in multi-agent control?
Jun 22, 2026Sophie Hall, Kai Zhang, Ilia Shilov +2Multi-Agent Control
Conflict-Aware Switching for CBF-CLF-Based Multi-Goal Navigation
Jun 19, 2026Rohan Walia, Kevin LeahyRobot Navigation Control Barrier Functions
Robotics Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
Leadership as Coordination Control: Behavioral Signatures and the Recovery-Advantage Boundary in Multi-Agent LLM Teams
Jun 17, 2026Haewoon KwakMulti-Agent LLM Systems Multi-Agent Coordination
Indiana University Bloomington
Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
Jun 9, 2026Saeid Jamshidi, Amin Nikanjam, Arghavan Moradi Dakhel +2Adversarial Attacks on LLMs Multi-Agent Control
SWAT Laboratory, Polytechnique Montréal, Montréal, QC, Canada · Huawei Distributed Scheduling and Data Engine Lab, Montr´eal, QC, Canada
Merging model-based control with multi-agent reinforcement learning for multi-agent cooperative teaming strategies
Jun 4, 2026Christian Llanes, Spencer W. Jensen, Samuel CooganMulti-Agent Control Multi-Robot Coordination
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. · Sandia National Laboratories, Albuquerque, NM 87123, USA.
RSC: Decentralized Rigid Formation Flocking for Large-Scale Swarms via Hybrid Predictive Control and Online Reconfiguration
Jun 2, 2026Ganyu Zou, Linhan Wang, Chen Dai +2Robotic Control Multi-Robot Formation Control
Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters
Jun 1, 2026Youssef Mahran, Zeyad Gamal, Aamir Ahmad +1Multi-Agent Control Quadrotor Control
Mechatronics Engineering Department, German University in Cairo (GUC), Egypt · Institute of Flight Mechanics and Control (IFR), Head of Flight Robotics, University of Stuttgart, Germany · Faculty of EMS, Head of Mechatronics Engineering Department, German University in Cairo (GUC), Egypt
Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
Jun 1, 2026Zixuan Jin, Wenzhuo Zhang, Shuxian Quan +4Multi-Robot Systems Swarm Robotics
School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China. · Shunde Innovation School, University of Science and Technology Beijing, 528399, Foshan Guangdong, China.
Distributed Non-Uniform Scaling Control of Multi-Agent Formation with Dynamic Agent Joining
May 28, 2026Tao He, Gangshan JingMulti-Robot Formation Control Multi-Agent Control
School of Automation, Chongqing University, Chongqing, China
SPIN: Decentralized Swarm Control via Tensorized Policy Coordination
May 25, 2026Zhaowen FanMulti-Agent Coordination Swarm Robotics
Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System
May 25, 2026Yuhui Hu, Dong Zhou, Kaihong Ouyang +3Robotic Control Reinforcement Learning
School of Astronautics, Harbin Institute of Technology, Harbin, 150001, China · School of Automation, Chongqing University, Chongqing, 400044, China · School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
ReactiveGWM: Flexible Control and NPC Reactivity in Game World Models
May 14, 2026Zeqing Wang, Danze Chen, Zhaohu Xing +4World Models Multi-Agent Control
Tencent · National University of Singapore · The Hong Kong University of Science and Technology (Guangzhou) +3
Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity
May 12, 2026Zijiang Yan, Hao Zhou, Wael Jaafar +4Multi-Objective Reinforcement Learning Language Model-Based Control
Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games
May 8, 2026Xiangyu Liu, Liang Zhang, Bo Jin +1Sequential Decision Making Hierarchical RL
Dalian University of Technology
Power-Efficiency and Scalability Analysis of Magnetically-Actuated Satellite Swarms via Convex Optimization
May 7, 2026Yuta Takahashi, Seang Shim, Hiraku Sakamoto +1Multi-Robot Systems Multi-Agent Control
Mechanical Engineering, Institute of Science Tokyo, Meguro-ku, Tokyo 152-8550, Japan · Department of Space and Astronautical Science, The Graduate University for Advanced Studies, Sagamihara, Kanagawa 252-5210, Japan · Spacecraft Engineering, Institute of Space and Astronautical Science, Sagamihara, Kanagawa 252-5210, Japan
Passive Fault Tolerance through Tension-to-Thrust Feed-Forward: Hybrid Input-to-State Stability for Decentralized Multi-UAV Slung-Load Transport under Abrupt Cable Severance
May 6, 2026Hadi Hajieghrary, Paul SchmittMulti-Robot Systems Robot Failure Recovery
Independent Researcher · Reynolds & Moore
Autonomous Synchronization of Discrete-Time Heterogeneous Multiagent Systems
May 6, 2026Wei Hu, Quanyi LiangMulti-Agent Control Multi-Agent Systems
School of Mathematical Sciences, Beihang University, Beijing 100191, China
A Scaled Three-Vehicle Platooning Platform
Apr 28, 2026Kaiyue Lu, Qiaoxuan Zhang, Yukun LuIntelligent Transportation Systems Autonomous Driving
Dept. of Mechanical Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility
Apr 27, 2026Yihong Zhou, Hongtai Zeng, Thomas MorstynMulti-Agent Control Constrained RL
Department of Engineering Science, University of Oxford
MultiWorld: Scalable Multi-Agent Multi-View Video World Models
Apr 20, 2026Haoyu Wu, Jiwen Yu, Yingtian Zou +1Multi-View Consistency Action-Conditioned World Models
The University of Hong Kong · Sreal AI
Shepherding UAV Swarm with Action Prediction Based on Movement Constraints
Apr 19, 2026Yusuke Tsunoda, Yusuke Goto, Takao SatoSwarm Robotics UAV Navigation
Graduate School of Engineering, University of Hyogo, Hyogo, Japan
Logic-Based Verification of Task Allocation for LLM-Enabled Multi-Agent Manufacturing Systems
Apr 18, 2026Jonghan Lim, Mostafa Tavakkoli Anbarani, Rômulo Meira-Góes +1Multi-Agent Control Formal Verification
Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, USA · Department of Mechanical Engineering, Pennsylvania State University, State College, USA · Department of Electrical Engineering, Pennsylvania State University, State College, USA

Jul 8, 2026, cs.ROJ/K move · Enter open · S save

Learning Spatiotemporal Tubes for Full Class of Signal Temporal Logic Tasks for Control of Unknown Systems under Input Constraints

Ahan Basu, Ratnangshu Das, Soumyodipta Nath, Siyuan Liu+1

Centre for Cyber-Physical Systems Indian Institute of Science, Bengaluru, India · Electrical Engineering Department, Eindhoven Institute of Technology, Netherlands

Signal Temporal Logic Multi-Agent Control Feedback Control

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This paper presents a Spatiotemporal Tube (STT)-based control framework for general unknown nonlinear Euler-Lagrange (EL) systems subject to input constraints, with the objective of satisfying Signal Temporal Logic (STL) specifications, where confinement of the system trajectory within the STT guarantees the satisfaction of the corresponding STL task. For both single and multi-agent scenarios, the STT corresponding to each agent is modeled as a time-varying ball, whose center and radius are jointly parameterized using a physics-informed neural network (PINN). The robustness metric associated with the STL specification corresponding to the agents is incorporated into the training process as a loss function, enabling the learned tube to encode task-level temporal requirements. For a multi-agent scenario, we introduce an additional robustness metric corresponding to the global task, which, when satisfied, ensures the tubes do not collide with each other. To ensure that the system trajectory remains within the learned STT and thereby satisfies the local and global STL specifications, we propose a control strategy that explicitly accounts for input constraints. In particular, a closed-form control law is developed to keep the trajectory inside the tube while regulating the motion of the tube by enforcing bounds on its evolution depending on the input constraints of the system. The proposed approach has been validated over several case studies.

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