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.

该论文提出了一种利用大型语言模型的通信和协作的分散式多代理导航框架，通过设计通信触发的动态领导组织结构，实现更快的团队共识，减少通信次数，提高导航效果和协同探索效率，在多目标导航任务中无冲突且具有稳健性，即使团队规模激增。

CAMON：基于 LLM 对话的多对象导航协作智能体

CAMON: Cooperative Agents for Multi-Object Navigation with LLM-based  Conversations

This work views the multi-agent system and its surrounding environment as a
co-evolving system, where the behavior of one affects the other. The goal is to
take both agent actions and environment configurations as decision variables,
and optimize these two components in a coordinated manner to improve some
measure of interest. Towards this end, we consider the problem of decentralized
multi-agent navigation in cluttered environments. By introducing two
sub-objectives of multi-agent navigation and environment optimization, we
propose an $\textit{agent-environment co-optimization}$ problem and develop a
$\textit{coordinated algorithm}$ that alternates between these sub-objectives
to search for an optimal synthesis of agent actions and obstacle configurations
in the environment; ultimately, improving the navigation performance. Due to
the challenge of explicitly modeling the relation between agents, environment
and performance, we leverage policy gradient to formulate a model-free learning
mechanism within the coordinated framework. A formal convergence analysis shows
that our coordinated algorithm tracks the local minimum trajectory of an
associated time-varying non-convex optimization problem. Extensive numerical
results corroborate theoretical findings and show the benefits of
co-optimization over baselines. Interestingly, the results also indicate that
optimized environment configurations are able to offer structural guidance that
is key to de-conflicting agents in motion.

通过引入多智能体系统和环境协同演化的观点，本研究提出了一个多智能体导航的去中心化问题，通过协同算法交替优化智能体行为和环境配置，选择最优的智能体动作和障碍物配置，以提高导航性能。通过策略梯度方法，在协同框架中建立了无模型学习机制，并进行了收敛性分析和对比实验，结果显示优化的环境配置对解决智能体运动冲突至关重要。

分散式多智能体导航的环境和政策协同优化

Co-Optimization of Environment and Policies for Decentralized  Multi-Agent Navigation

We investigate the problem of decentralized multi-agent navigation tasks,
where multiple agents need to reach initially unassigned targets in a limited
time. Classical planning-based methods suffer from expensive computation
overhead at each step and offer limited expressiveness for complex cooperation
strategies. In contrast, reinforcement learning (RL) has recently become a
popular paradigm for addressing this issue. However, RL struggles with low data
efficiency and cooperation when directly exploring (nearly) optimal policies in
the large search space, especially with an increased agent number (e.g., 10+
agents) or in complex environments (e.g., 3D simulators). In this paper, we
propose Multi-Agent Scalable GNN-based P lanner (MASP), a goal-conditioned
hierarchical planner for navigation tasks with a substantial number of agents.
MASP adopts a hierarchical framework to divide a large search space into
multiple smaller spaces, thereby reducing the space complexity and accelerating
training convergence. We also leverage graph neural networks (GNN) to model the
interaction between agents and goals, improving goal achievement. Besides, to
enhance generalization capabilities in scenarios with unseen team sizes, we
divide agents into multiple groups, each with a previously trained number of
agents. The results demonstrate that MASP outperforms classical planning-based
competitors and RL baselines, achieving a nearly 100% success rate with minimal
training data in both multi-agent particle environments (MPE) with 50 agents
and a quadrotor 3-dimensional environment (OmniDrones) with 20 agents.
Furthermore, the learned policy showcases zero-shot generalization across
unseen team sizes.

我们提出了基于多智能体可扩展图神经网络的分层规划器 (MASP)，用于具有大量智能体的导航任务，通过将搜索空间分成多个较小的空间以减少复杂度和加快训练收敛速度，并利用图神经网络建模智能体和目标之间的相互作用，提高目标实现能力，并在未见过的团队规模工作场景中展示了零 - shot 泛化能力。