[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84777-en":3,"doc-seo-84777-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84777,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales","Deploying robot teams in real-world conditions requires simultaneous adaptation to unseen environments, unknown partners, and changing team sizes, while many existing methods treat these factors separately under a closed-world assumption with fixed teammates. The work formalizes open adaptive multi-robot teaming and introduces a hypergraphic-form game model to represent team-level cooperative relationships beyond pairwise interactions for dynamic coordination structure inference. It further proposes HOLA, which expands environment and partner diversity during training, and shows superior results on cooperative pursuit with multi-drone and multiquadruped platforms. Policies transfer to physical hardware without finetuning.","Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales  \nYang Li 1,* , Feng Xue2 , Fan Mo3 , Yunhao Liu4 , Jianhong Wang5 , Ying Wen 1 , Qingrui Zhang2 , Shaoshuai Mou6 , Wei Pan7,*  \n1 Shanghai Jiao Tong University 2 Sun Yat-sen University 3National University of Singapore  \n4 Genisom AI 5University of Bristol 6Purdue University 7Newcastle University  \n* Corresponding author: [wei.pan2@newcastle.ac.uk](wei.pan2@newcastle.ac.uk) , [yang.li.cs@sjtu.edu.cn](yang.li.cs@sjtu.edu.cn)  \narXiv :2607 .04972v 1 [ cs .RO] 6 Jul 2026  \nAbstract—Deploying robot teams in the real world requires simultaneous adaptation to unseen environments, unknown partners, and varying team sizes, yet existing approaches often address these challenges in isolation under the closed-world assumption of fixed teammates. We formalize this as open adaptive multi-robot teaming and propose a hypergraphic-form game formulation that captures team-level cooperative relationships beyond pairwise interactions, providing a principled foundation for coordination structure inference when team composition changes dynamically within episodes. Unlike graph neural network architectures, this is a game-theoretic construct for modeling strategic interactions and payoff structures among agents. Building on this formulation, we develop the Hypergraphic Open-ended Learning Algorithm (HOLA), which progressively expands partner and environment diversity during training rather than optimizing for fixed configurations. Evaluated on cooperative pursuit with multi-drone and multi-quadruped platforms, HOLA outperforms all baselines across all three adaptability dimensions. Learned policies transfer directly to physical hardware without finetuning, with successful deployments on Crazyflie and Zsibot L1 platforms confirming robust real-world coordination in novel environments with unseen teammates.  \nIndex Terms—open adaptive teaming, multi-robot collaboration, cooperative pursuit, multi-drone collaboration, multiquadruped collaboration  \nI. INTRODUCTION  \nMulti-robot systems are rapidly transitioning from controlled laboratory settings to unpredictable real-world environments such as disaster zones, dynamic warehouses, and surveillance networks, where the fundamental assumptions underlying conventional coordination mechanisms breakdown [1]–[3] . In these settings, robots cannot rely on familiar terrain, known teammates, or stable team configurations. A search-and-rescue drone may lose communication with its squad and must instantly coordinate with an unfamiliar ground unit; a warehouse robot must seamlessly integrate a newly deployed agent mid-task; a border patrol team must reorganize on-the-fly as units fail or reinforcements arrive. These scenarios share a common challenge that existing approaches fail to address: robots must simultaneously generalize across unseen environments, coordinate with unknown partners, and scale to varying team sizes—all in real time, without retraining. We formalise this challenge as the open adaptive multi-robot teaming problem.  \nFig. 1 contrasts the dominant learning paradigm with the open adaptive teaming challenge. Centralized training with  \nOpen Adaptive Teaming  \nP5  \nLearner  Evader  \nP3  \nEvader  \nLearner P4  \nTraining Phase  \nTest Phase: fixed team, known teammates, seen environments  \nTest  \nPhase: open team, unknown teammates, unseen environments  \nAfter k steps  \nP1  \nLearner  \nP2  \nEvader Evader Learner  \nFig. 1: Comparison of Traditional CTDE and Open Adaptive Teaming Paradigm. In conventional CTDE (top right), the test phase maintains a fixed team composition with known teammates in previously seen environments. In contrast, open adaptive teaming (bottom) presents significantly greater challenges: agents must coordinate in open teams with unseen teammates and dynamic scale in unseen environments during online interaction.  \ndecentralized execution (CTDE) [4]–[6] trains a fixed set of agents jointly, allowing th","cbCaiow16y0s1BAm","https://ap.wps.com/l/cbCaiow16y0s1BAm","pdf",9179033,1,13,"English","en",105,"# Introduction\n## Closed-world assumptions in CTDE\n## Open adaptive teaming problem formulation","[{\"question\":\"What problem does open adaptive multi-robot teaming address?\",\"answer\":\"It addresses the need for robots to generalize across unseen environments, coordinate with unknown partners, and scale to varying team sizes in real time without retraining.\"},{\"question\":\"How does the proposed hypergraphic-form game formulation differ from graph neural network approaches?\",\"answer\":\"It is a game-theoretic construct that models strategic interactions and payoff structures among agents, enabling team-level coordination modeling beyond pairwise relationships rather than using a purely neural architecture perspective.\"},{\"question\":\"What is HOLA and how does it achieve adaptability during training?\",\"answer\":\"HOLA progressively expands partner and environment diversity during training, avoiding optimization for fixed configurations so the learned policies remain robust when teams and environments change during 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problem does open adaptive multi-robot teaming address?","Question",{"text":75,"@type":76},"It addresses the need for robots to generalize across unseen environments, coordinate with unknown partners, and scale to varying team sizes in real time without retraining.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed hypergraphic-form game formulation differ from graph neural network approaches?",{"text":80,"@type":76},"It is a game-theoretic construct that models strategic interactions and payoff structures among agents, enabling team-level coordination modeling beyond pairwise relationships rather than using a purely neural architecture perspective.",{"name":82,"@type":73,"acceptedAnswer":83},"What is HOLA and how does it achieve adaptability during training?",{"text":84,"@type":76},"HOLA progressively expands partner and environment diversity during training, avoiding optimization for fixed configurations so the learned policies remain robust when teams and 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