Abstract
Task allocation can enable effective coordination of multi-robot teams to accomplish tasks that are intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user. In this work, we consider the challenge of forming effective coalitions for a given heterogeneous multi-robot team when task reward functions are unknown. To this end, we first formulate a new class of problems, dubbed COncurrent Constrained Online optimization of Allocation (COCOA). The COCOA problem requires online optimization of coalitions such that the unknown rewards of all the tasks are simultaneously maximized using a given multi-robot team with constrained resources. To address the COCOA problem, we introduce an online optimization algorithm, named Concurrent Multi-Task Adaptive Bandits (CMTAB), that leverages and builds upon continuum-armed bandit algorithms. Experiments involving detailed numerical simulations and a simulated emergency response task reveal that CMTAB can effectively trade-off exploration and exploitation to simultaneously and efficiently optimize the unknown task rewards while respecting the team's resource constraints.
Abstract (translated)
任务分配可以促使多个机器人团队有效地协调完成个人机器人无法完成的任务。然而，当前的任务分配方法通常假设任务要求或奖励函数已知并明确指定。在这项工作中，我们考虑了当任务奖励函数未知时如何构建有效的多方联盟的问题。为此，我们定义了一个新的问题类型，称为当前状态限制在线优化分配(COCOA)问题。COCOA问题需要在线优化联盟，使得使用给定的资源限制限制的多方机器人团队中的所有任务未知奖励都同时最大化。为了解决COCOA问题，我们介绍了一种在线优化算法，称为并发多任务自适应币种(CMTAB)，它基于连续币种算法并利用它。涉及详细数值模拟和模拟紧急情况响应任务的实验表明，CMTAB可以有效地进行探索和利用的权衡，同时尊重团队的资源限制，同时高效优化未知任务奖励。
URL
https://arxiv.org/abs/2305.15288
