Can Robots Teach Themselves to Team Up?

Author: Denis Avetisyan

Researchers have created a new benchmark to assess how well large language models can enable groups of robots to cooperate and self-organize without explicit programming.

Tool-RoCo provides a rigorous evaluation framework for agent-as-tool approaches to multi-robot cooperation using large language models.

While recent advances demonstrate the potential of large language models (LLMs) in multi-agent systems, evaluating true agent autonomy-beyond pre-defined orchestration-remains a significant challenge. This paper introduces Tool-RoCo: An Agent-as-Tool Self-organization Large Language Model Benchmark in Multi-robot Cooperation, a novel benchmark designed to assess LLM-driven cooperation and self-organization by framing agents as selectable tools within robotic tasks. Analysis using this benchmark reveals a current tendency for LLMs to maintain active agents rather than dynamically coordinating through tool-based activation and collaboration-with cooperative tools utilized in only a small fraction of interactions. Can we develop LLMs capable of more adaptive and efficient multi-agent coordination through nuanced tool selection and strategic agent activation?

The Inevitable Rise of Collaborative Robotics and the Demand for Rigorous Evaluation

The increasing complexity of real-world challenges, from disaster response and environmental monitoring to advanced manufacturing and agricultural automation, necessitates the deployment of multi-robot systems. These systems, comprised of numerous interacting agents, offer advantages in scalability, robustness, and efficiency that single robots simply cannot achieve. However, realizing this potential hinges on the development of robust coordination strategies. Effective coordination isn’t merely about preventing collisions; it demands seamless communication, task allocation, and dynamic adaptation to unforeseen circumstances. Each robot must understand its role within the collective, anticipate the actions of others, and adjust its behavior accordingly to achieve a shared objective. Without sophisticated coordination mechanisms, a swarm of robots can quickly devolve into a chaotic and unproductive collection of individuals, unable to tackle anything beyond the simplest of tasks. Therefore, advancements in multi-robot coordination are paramount to unlocking the full capabilities of these systems and enabling them to address increasingly intricate problems.

Current robotic benchmarks often prioritize short-term task completion within highly controlled environments, failing to adequately assess a multi-robot system’s capacity for sustained, independent operation in dynamic real-world scenarios. These evaluations typically measure performance on isolated skills, neglecting the crucial ability of robotic agents to adapt to unforeseen circumstances, recover from failures, and maintain cohesive collaboration over extended periods. Consequently, a high score on existing benchmarks doesn’t necessarily translate to reliable performance when deployed in complex, unpredictable environments – a significant limitation as robotic teams transition from research labs to practical applications. The absence of long-term evaluation metrics hinders progress towards truly autonomous multi-robot systems capable of handling the inherent uncertainties of real-world tasks and collaborating effectively without constant human intervention.

The emergence of Tool-RoCo signifies a critical advancement in multi-robot system evaluation, moving beyond simplistic task completion to assess a robot’s proficiency in utilizing tools within a collaborative framework. This benchmark prioritizes not merely the execution of a pre-programmed sequence, but the capacity of robotic agents to dynamically adapt to unforeseen challenges by intelligently selecting and employing tools to achieve shared objectives. By emphasizing tool manipulation and cooperative strategies, Tool-RoCo presents scenarios demanding a higher level of autonomy and problem-solving, forcing robots to demonstrate genuine understanding of their environment and the capabilities of both themselves and their teammates. This focus on collaborative tool use is paramount, as many real-world applications – from complex assembly lines to disaster response – inherently require robots to function as a cohesive unit, leveraging specialized tools to overcome obstacles and accomplish intricate tasks.

Agent Autonomy: The Foundation of Self-Organization and Intelligent Tool Use

Intelligent tool selection is foundational to agent functionality, allowing agents to dynamically identify and utilize appropriate resources to achieve specified objectives. This process involves assessing task requirements and mapping them to available tools based on functional capability and, potentially, performance metrics. Effective tool selection isn’t simply about having a diverse toolkit; it requires an agent to accurately evaluate the context of a given task and choose the tool that optimizes for efficiency, accuracy, or other defined criteria. The capacity for an agent to independently select and apply tools is directly correlated with its ability to address complex, multi-step problems and adapt to changing environmental conditions without explicit external direction.

The capacity for self-organization, quantified by the Self-Organization Ratio (SO), is a critical component of robust agent systems capable of addressing novel or unexpected situations. The SO represents the proportion of times an agent autonomously requests assistance from other agents or external resources when encountering a challenge it cannot resolve independently. Empirical data consistently demonstrates a high SO across diverse agent models, indicating a general propensity for agents to seek help rather than persisting with failing strategies. This behavior is not merely reactive; a consistently high SO suggests an inherent design principle for adaptability and resilience, allowing the system as a whole to overcome obstacles through distributed problem-solving even when individual agents lack complete information or capabilities. The ratio is calculated as $SO = \frac{\text{Number of assistance requests}}{\text{Total number of challenges encountered}}$.

System architectures employing self-organization utilize both centralized and decentralized approaches, each with implications for performance and scalability. Centralized self-organization relies on a coordinating entity to distribute tasks and manage agent activity, while decentralized cooperation allows agents to negotiate and collaborate directly. Empirical results indicate that while models demonstrate a high Self-Organization Ratio (SO) – indicating frequent requests for assistance – they consistently struggle with agent deactivation. Even when agents complete tasks or are no longer needed, the system often fails to effectively remove them from active participation, potentially leading to resource contention and reduced efficiency despite a high SO. This suggests that while agents are adept at identifying the need for assistance, the mechanisms for managing agent lifecycles and ensuring optimal resource allocation require further development.

Validating Autonomy Through Complex Tasks: Cabinet, Pack, and Sort

Tool-RoCo validation utilizes the Cabinet Task, Pack Task, and Sort Task as benchmarks for assessing complex coordination capabilities. These tasks require a system to integrate perception, planning, and manipulation skills to achieve a defined objective; the Cabinet Task involves opening cabinets and retrieving specified items, the Pack Task necessitates the efficient packing of multiple objects into a container, and the Sort Task demands the categorization and placement of items based on defined criteria. Successful completion of these tasks demonstrates an agent’s ability to handle non-trivial problems requiring a sequence of coordinated actions and adaptations to varying conditions.

The “Agent-as-Tool” paradigm enables a system where autonomous agents are not solely independent actors, but can function as modular components within a larger workflow, leveraging the specialized capabilities of other agents. This approach moves beyond simple task allocation to facilitate a dynamic composition of agent skills; one agent can call upon another to perform a sub-task, effectively utilizing it as a tool to achieve a more complex goal. The implementation of this concept requires a standardized interface for agent interaction, allowing for seamless integration and interoperability between different agent types and functionalities, and is a key element in scaling autonomous systems to tackle increasingly intricate problems.

The evaluation benchmark utilizes both Common Tools – functionalities readily available within the agent’s base capabilities – and Cooperative Tools, requiring interaction with other agents to complete tasks. Performance is quantified using the Cooperative Tool Ratio (CT), which represents the proportion of tasks completed leveraging cooperative functionalities. Current results with GPT-5 achieve a maximum CT of 9.28%, indicating a limited but demonstrable capacity for collaborative problem-solving within the defined benchmark tasks. This ratio suggests that while the agent can utilize cooperative tools, the extent of its reliance on, and benefit from, collaborative interactions remains relatively low.

Formalizing the Cooperative Landscape: Dec-POMDP and LLM Integration

The Tool-RoCo environment utilizes Dec-POMDPs (Decentralized Partially Observable Markov Decision Processes) to formalize cooperative tasks and enable quantifiable performance evaluation. A Dec-POMDP extends the standard MDP framework to encompass multiple agents, partial observability of the environment, and decentralized execution; each agent maintains its own belief state based on its individual observations and actions. This formalism allows for the rigorous definition of state, action, observation spaces, transition dynamics, reward functions, and discount factors, creating a well-defined mathematical model of the cooperative problem. By framing tasks as Dec-POMDPs, Tool-RoCo provides a standardized basis for comparing different multi-agent learning algorithms and assessing their effectiveness in complex, collaborative scenarios, focusing on metrics derived directly from the Dec-POMDP solution.

Large Language Models (LLMs), specifically GPT-4 and GPT-5, are utilized as control mechanisms for agents within the Tool-RoCo framework and to manage inter-agent communication. Empirical data demonstrates GPT-5’s proficiency in this role, achieving a Tool Calling Success Rate exceeding 93.04%. This metric represents the percentage of instances where the LLM correctly identifies and requests the appropriate tool for a given task. Furthermore, the Execution Validity of GPT-5 is reported at 75.90%, indicating the proportion of tool executions that successfully complete the intended action as directed by the LLM. These performance indicators quantify the LLM’s ability to both plan and reliably implement actions within the cooperative environment.

The integration of Large Language Models (LLMs) within the Dec-POMDP framework enables the assessment of multi-agent systems operating in complex, extended cooperative tasks. Traditional evaluation methods often struggle with the challenges of long-horizon dependencies and partial observability inherent in realistic scenarios. By leveraging LLMs for agent control and communication, researchers can now simulate and analyze agent behavior over prolonged interactions, evaluating performance metrics such as task completion rates, resource utilization, and the emergence of cooperative strategies. This approach facilitates a more comprehensive understanding of LLM-driven agents’ capabilities in dynamic, partially observable environments, moving beyond isolated task completion to assess sustained cooperative performance.

Future Directions: Scaling and Generalizing Cooperative Intelligence

Tool-RoCo establishes a rigorous testing ground for large language model (LLM) based multi-agent systems, moving beyond simple benchmarks to assess collaborative performance in dynamic scenarios. This platform isn’t merely about measuring success or failure; it provides granular data on how agents cooperate, identifying strengths and weaknesses in communication, task allocation, and conflict resolution. By offering a standardized environment with controllable complexity, Tool-RoCo allows researchers to systematically refine agent designs and training methodologies, fostering iterative improvements in cooperative strategies. The platform’s robustness stems from its ability to simulate realistic challenges and evaluate agents across diverse metrics, ultimately accelerating progress towards more effective and adaptable collaborative AI systems.

Researchers are now directing efforts towards expanding the capabilities of these cooperative systems by increasing both team size and environmental complexity. Current investigations involve testing the limits of Tool-RoCo with significantly larger numbers of agents, assessing how communication and coordination strategies adapt-or fail-as teams grow. Simultaneously, the platform is being utilized to simulate more intricate scenarios, incorporating dynamic elements and unpredictable challenges that demand greater adaptability and problem-solving skills from the agents. This scaling process isn’t simply about increasing numbers; it’s about understanding the emergent properties of larger teams and identifying the bottlenecks that prevent effective collaboration in complex settings, ultimately paving the way for robust and versatile artificial intelligence.

The pursuit of genuinely generalizable cooperative intelligence represents a significant leap beyond task-specific multi-agent systems. This ambition envisions artificial intelligence capable of collaboratively solving novel problems across diverse domains – from coordinating disaster relief efforts and optimizing complex logistical networks to accelerating scientific discovery and fostering creative endeavors. Such a system wouldn’t simply perform pre-programmed tasks, but rather adapt to unforeseen challenges, learn from interactions with both humans and other agents, and synthesize solutions through robust communication and shared understanding. Achieving this requires not merely scaling existing architectures, but fundamentally rethinking how agents represent knowledge, negotiate strategies, and build trust – ultimately paving the way for AI collaborators capable of augmenting human capabilities in previously unimaginable ways.

The pursuit of robust multi-robot cooperation, as demonstrated by Tool-RoCo, demands more than simply achieving a functional outcome; it necessitates a provably correct system. The benchmark’s emphasis on self-organization and tool usage as agents directly mirrors a fundamental principle of elegant design. As Barbara Liskov stated, “Programs must be correct, not just work.” This isn’t merely a matter of passing tests, but of ensuring the underlying logic – the invariant – is transparent and reliable. If a multi-robot system feels like it’s magically coordinating, it likely indicates a hidden dependency or unverified assumption, a clear violation of mathematical purity. Tool-RoCo, by its very nature, forces a rigorous examination of these invariants.

The Horizon of Collective Intelligence

The Tool-RoCo benchmark, while a necessary articulation of current limitations, merely exposes the chasm between algorithmic execution and genuine collective intelligence. The capacity for an agent to function as a tool does not equate to understanding the purpose of the orchestration. Future work must address this semantic deficit – focusing not simply on successful task completion, but on verifiable, mathematically sound principles of emergent behavior. The current reliance on empirical observation, on ‘seeing if it works’, is a concession to expediency, not a foundation for robust systems.

A crucial, and largely unaddressed, challenge lies in defining and quantifying ‘self-organization’ itself. Is it merely stochastic convergence towards a functional state, or does it require demonstrable optimality – a provable minimization of resources, maximization of efficiency, or adherence to some other quantifiable metric? The field demands a move beyond descriptive analyses, toward formal models that predict – and therefore guarantee – cooperative outcomes.

Ultimately, the pursuit of multi-agent cooperation necessitates a re-evaluation of agency itself. To treat agents as tools is a pragmatic starting point, but true intelligence resides not in instrumentality, but in the capacity for abstract reasoning and independent goal formulation. The benchmark, therefore, should evolve to assess not just how agents cooperate, but why – and whether that ‘why’ aligns with principles of logical consistency and mathematical elegance.

Original article: https://arxiv.org/pdf/2511.21510.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/