Cooperatively accomplishing embodied tasks by multiple robots has consistently been a highly challenging area of research. Recent studies mainly focus on embodied manipulation cooperation among robotic arms or formation control over the upper level within a group of mobile robots [1, 2]. Nevertheless, multi-agent cooperation via whole-body and end-to-end locomotion control is rarely studied. Some previous works showcase the manipulation via locomotion [3] but are only tested on two agent systems, and the scalability of this method is still agnostic for migration to any number of agent populations. In this work, we aim to realize more complex embodied multi-agent cooperation by learning a distributed hierarchical locomotion control system, decomposing the complex and coupled behaviours while maintaining the potential for unlimited expansion on the swarm. As the foundation for implementation and validation, we construct three scenarios in IsaacSim/Gym [4] as benchmarks for embodied cooperation study. Concurrently, training a robot for a specific function can be effectively achieved through reinforcement learning (RL), like learning movement patterns [5], interactive behaviours [6], as well as logical inference in games [7]. Although RL provides a recognized powerful exploration capability and tremendous progress has been made in sampling efficiency [4], finding and mastering a sequence of sophisticated tasks through searching remains a challenging problem. Hierarchical reinforcement learning (HRL) alleviates this to a certain extent, aiming to understand the logical relationships among "control, action, behaviour, dynamic outcomes, and feedback" in a segmented manner.

We study a multi-agent decision problem in population games, where agents select from multiple available strategies and continually revise their selections based on the payoffs associated with these strategies. Unlike conventional population game formulations, we consider a scenario where agents must estimate the payoffs through local measurements and communication with their neighbors. By employing task allocation games -- dynamic extensions of conventional population games -- we examine how errors in payoff estimation by individual agents affect the convergence of the strategy revision process. Our main contribution is an analysis of how estimation errors impact the convergence of the agents' strategy profile to equilibrium. Based on the analytical results, we propose a design for a time-varying strategy revision rate to guarantee convergence. Simulation studies illustrate how the proposed method for updating the revision rate facilitates convergence to equilibrium.

Ad hoc teamwork poses a challenging problem, requiring the design of an agent to collaborate with teammates without prior coordination or joint training. Open ad hoc teamwork (OAHT) further complicates this challenge by considering environments with a changing number of teammates, referred to as open teams. One promising solution in practice to this problem is leveraging the generalizability of graph neural networks to handle an unrestricted number of agents with various agent-types, named graph-based policy learning (GPL). However, its joint Q-value representation over a coordination graph lacks convincing explanations. In this paper, we establish a new theory to understand the representation of the joint Q-value for OAHT and its learning paradigm, through the lens of cooperative game theory. Building on our theory, we propose a novel algorithm named CIAO, based on GPL's framework, with additional provable implementation tricks that can facilitate learning. The demos of experimental results are available on https://sites.google.com/view/ciao2024, and the code of experiments is published on https://github.com/hsvgbkhgbv/CIAO.

Abstract: A key requirement in robotics is the ability to simultaneously self-localize and map a previously unknown environment, relying primarily on onboard sensing and computation. Achieving fully onboard accurate simultaneous localization and mapping (SLAM) is feasible for high-end robotic platforms, whereas small and inexpensive robots face challenges due to constrained hardware, therefore frequently resorting to external infrastructure for sensing and computation. The challenge is further exacerbated in swarms of robots, where coordination, scalability, and latency are crucial concerns. This work introduces a decentralized and lightweight collaborative SLAM approach that enables mapping on virtually any robot, even those equipped with low-cost hardware, including miniaturized insect-size devices. Moreover, the proposed solution supports large swarm formations with the capability to coordinate hundreds of agents. To substantiate our claims, we have successfully implemented collaborative SLAM on centimeter-size drones weighing only 46 grams. Remarkably, we achieve results comparable to high-end state-ofthe-art solutions while reducing the cost, memory, and computation requirements by two orders of magnitude. Our approach is innovative in three main aspects. First, it enables onboard infrastructure-less collaborative mapping with a lightweight and cost-effective solution in terms of sensing and computation. Second, we optimize the data traffic within the swarm to support hundreds of cooperative agents using standard wireless protocols such as ultra-wideband (UWB), Bluetooth, or WiFi. Last, we implement a distributed swarm coordination policy to decrease mapping latency and enhance accuracy. INTRODUCTION Nowadays, swarms of autonomous robots find applications in many sectors, from industry to civil markets, including biomedical and healthcare (1, 2). Key tasks such as perception or mapping can be carried out more effectively and at lower latency by a swarm than by a single agent (3). However, the design of a collaboration scheme between the agents of a swarm is still an unsolved challenge in many robotics applications (2).

Autonomous agents driven by Large Language Models (LLMs) offer enormous potential for automation. Early proof of this technology can be found in various demonstrations of agents solving complex tasks, interacting with external systems to augment their knowledge, and triggering actions. In particular, workflows involving multiple agents solving complex tasks in a collaborative fashion exemplify their capacity to operate in less strict and less well-defined environments. Thus, a multi-agent approach has great potential for serving as a backbone in many industrial applications, ranging from complex knowledge retrieval systems to next generation robotic process automation. Given the reasoning abilities within the current generation of LLMs, complex processes require a multi-step approach that includes a plan of well-defined and modular tasks. Depending on the level of complexity, these tasks can be executed either by a single agent or a group of agents. In this work, we focus on designing a flexible agent engineering framework with careful attention to planning and execution, capable of handling complex use case applications across various domains. The proposed framework provides reliability in industrial applications and presents techniques to ensure a scalable, flexible, and collaborative workflow for multiple autonomous agents working together towards solving tasks.

This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

Wildfires pose a severe challenge to ecosystems and human settlements, exacerbated by climate change and environmental factors. Traditional wildfire modeling, while useful, often fails to adapt to the rapid dynamics of such events. This report introduces the (Wildfire Autonomous Response and Prediction Using Cellular Automata) WARP-CA model, a novel approach that integrates terrain generation using Perlin noise with the dynamism of Cellular Automata (CA) to simulate wildfire spread. We explore the potential of Multi-Agent Reinforcement Learning (MARL) to manage wildfires by simulating autonomous agents, such as UAVs and UGVs, within a collaborative framework. Our methodology combines world simulation techniques and investigates emergent behaviors in MARL, focusing on efficient wildfire suppression and considering critical environmental factors like wind patterns and terrain features.

Large Language Models (LLMs) have increasingly been utilized in social simulations, where they are often guided by carefully crafted instructions to stably exhibit human-like behaviors during simulations. Nevertheless, we doubt the necessity of shaping agents' behaviors for accurate social simulations. Instead, this paper emphasizes the importance of spontaneous Figure 1: (Depicted by GPT-4o) Two potential scenarios phenomena, wherein agents deeply engage in during a fire. People might panic and rush into crowds, contexts and make adaptive decisions without trying to exit first (left) or may stay calm, keep in line, explicit directions. We explored spontaneous and encourage others (right). In this study, we explore cooperation across three competitive scenarios whether LLM agents can simulate the gradual transition and successfully simulated the gradual emergence from non-cooperative to cooperative behaviors of agents. of cooperation, findings that align closely with human behavioral data.

The emergence of Large Language Models (LLMs) and advancements in Artificial Intelligence (AI) offer an opportunity for computational social science research at scale. Building upon prior explorations of LLM agent design, our work introduces a simulated agent society where complex social relationships dynamically form and evolve over time. Agents are imbued with psychological drives and placed in a sandbox survival environment. We conduct an evaluation of the agent society through the lens of Thomas Hobbes's seminal Social Contract Theory (SCT). We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security. Our experiments unveil an alignment: Initially, agents engage in unrestrained conflict, mirroring Hobbes's depiction of the state of nature. However, as the simulation progresses, social contracts emerge, leading to the authorization of an absolute sovereign and the establishment of a peaceful commonwealth founded on mutual cooperation. This congruence between our LLM agent society's evolutionary trajectory and Hobbes's theoretical account indicates LLMs' capability to model intricate social dynamics and potentially replicate forces that shape human societies. By enabling such insights into group behavior and emergent societal phenomena, LLM-driven multi-agent simulations, while unable to simulate all the nuances of human behavior, may hold potential for advancing our understanding of social structures, group dynamics, and complex human systems.

Visual navigation tasks are critical for household service robots. As these tasks become increasingly complex, effective communication and collaboration among multiple robots become imperative to ensure successful completion. In recent years, large language models (LLMs) have exhibited remarkable comprehension and planning abilities in the context of embodied agents. However, their application in household scenarios, specifically in the use of multiple agents collaborating to complete complex navigation tasks through communication, remains unexplored. Therefore, this paper proposes a framework for decentralized multi-agent navigation, leveraging LLM-enabled communication and collaboration. By designing the communication-triggered dynamic leadership organization structure, we achieve faster team consensus with fewer communication instances, leading to better navigation effectiveness and collaborative exploration efficiency. With the proposed novel communication scheme, our framework promises to be conflict-free and robust in multi-object navigation tasks, even when there is a surge in team size.