FORMICA：面向通信-free多机器人任务分配的决策聚焦学习
FORMICA: Decision-Focused Learning for Communication-Free Multi-Robot Task Allocation
AI总结：
FORMICA通过学习预测队友报价实现多机器人任务分配，无需通信，提升系统奖励17%，具备良好泛化能力。
摘要：
大多数多机器人任务分配方法依赖通信来解决冲突并达成一致的分配。在带宽有限、基础设施退化或存在对抗干扰的环境中，现有方法性能急剧下降。我们介绍了一种基于学习的框架，实现高质量的任务分配而无需机器人之间的通信。关键思想是机器人通过预测队友的报价进行隐式协调：如果每个机器人能够预见到对任务的竞争，它就可以相应地调整自己的选择。我们的方法预测报价分布以纠正分析性均场近似中的系统误差。虽然分析预测假设理想化条件（均匀分布、已知报价函数），我们的学习方法适应于任务聚类和空间异质性。受Smart Predict-then-Optimize（SPO）的启发，我们训练预测器端到端以最小化任务分配遗憾，而不是预测误差。为了扩展到大规模群体，我们开发了一种均场近似方法，其中每个机器人预测竞争报价的分布而不是个体报价，将复杂度从O(NT)降低到O(T)。我们称我们的方法为FORMICA：面向场的遗憾最小化隐式协调算法。实验表明，FORMICA显著优于自然分析基线。在16个机器人和64个任务的场景中，我们的方法提高了系统奖励17%，接近最优MILP解决方案。当部署在更大场景（256个机器人，4096个任务）时，相同模型的性能提高了7%，显示了强大的泛化能力。训练仅需在笔记本上21秒，使快速适应新环境成为可能。
英文摘要：
Most multi-robot task allocation methods rely on communication to resolve conflicts and reach consistent assignments. In environments with limited bandwidth, degraded infrastructure, or adversarial interference, existing approaches degrade sharply. We introduce a learning-based framework that achieves high-quality task allocation without any robot-to-robot communication. The key idea is that robots coordinate implicitly by predicting teammates' bids: if each robot can anticipate competition for a task, it can adjust its choices accordingly. Our method predicts bid distributions to correct systematic errors in analytical mean-field approximations. While analytical predictions assume idealized conditions (uniform distributions, known bid functions), our learned approach adapts to task clustering and spatial heterogeneity. Inspired by Smart Predict-then-Optimize (SPO), we train predictors end-to-end to minimize Task Allocation Regret rather than prediction error. To scale to large swarms, we develop a mean-field approximation where each robot predicts the distribution of competing bids rather than individual bids, reducing complexity from $O(NT)$ to $O(T)$. We call our approach FORMICA: Field-Oriented Regret-Minimizing Implicit Coordination Algorithm. Experiments show FORMICA substantially outperforms a natural analytical baseline. In scenarios with 16 robots and 64 tasks, our approach improves system reward by 17% and approaches the optimal MILP solution. When deployed on larger scenarios (256 robots, 4096 tasks), the same model improves performance by 7%, demonstrating strong generalization. Training requires only 21 seconds on a laptop, enabling rapid adaptation to new environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
