CAREER: Crowdsourcing for Multirobot Coordination

人类组成的团队特别擅长协调。然而，机器人团队在协调方面非常笨拙，需要大量的沟通和计算。对这种基础设施的依赖给将机器人团队引入实际应用程序带来了重大障碍。该项目正在寻求一种综合研究、教育和推广方法，以开发受人类协调启发的新型、数据驱动的多机器人协调算法。作为基于广泛背景、记忆和感知做出决定的认知生物，人类的能力很难转移到机器人身上。为了促进这种转移，该项目正在开发一个在线众包应用程序，让参与者创建一个全局结构，比如一个形状。该应用程序通过限制可用的信息和操作，将参与者限制为类似机器人的功能。该应用程序将提供分布式机器人团队能力的忠实表现，并将用于深入了解人类协调，然后将其转移到多机器人系统中。所提议的工作的总体目标是开发基于人类协作的多机器人协调的新方法，该方法基于从众包在线应用程序收集的数据中学习的模型。为此，研究目标是：(1)利用在线多人界面生成的数据，阐明在处理紧密耦合任务的分布式团队中，上下文（通信和传感）与结果之间的关系；(2)使用统计方法识别分布式机器人团队解决类似共享目标问题的参数；(3)利用从众包应用程序中收集的数据，利用深度学习架构推断出分布式机器人团队解决紧密耦合问题的各种协调模型集合；(4)通过结合仿真、硬件和混合现实实验来评估这些模型在解决紧密耦合问题方面的成功，从而验证这些模型。

Teams of humans are exceptionally good at coordination. Teams of robots, however, are extremely clumsy at coordination, requiring extensive communication and computation. Reliance on this infrastructure poses a significant roadblock to bringing robot teams into real-world applications. This project is pursuing an integrated research, education, and outreach approach for developing novel, data-driven algorithms for multi-robot coordination, inspired by human coordination. As cognitive beings that make decisions based on broad context, memory, and sensing, human capabilities are challenging to transfer to robotics. To facilitate this transfer, the project is developing an online crowdsourcing application that tasks participants with creating a global structure, such as a shape. The application constrains participants to robot-like capabilities by limiting available information and actions. The application will provide a faithful representation of the capabilities of distributed teams of robots, and will be used to gain insights into human coordination that can then be transferred to a multi-robot system.The overarching goal of the proposed work is to develop novel methodologies for multi robot coordination firmly grounded in human collaboration, based on models learned from data collected via a crowdsourced online application. To this end, the research objectives are (1) to explicate the relationship between context (communication and sensing) and outcomes in distributed teams of humans working on tightly coupled tasks using data generated from an online multi-person interface; (2) to identify, using statistical methods, parameters for distributed teams of robots solving similar shared objective problems; (3) to infer, using deep learning architectures, diverse ensembles of coordination models for distributed teams of robots solving tightly coupled problems using the data collected from the crowdsourcing application; and (4) to validate these models by evaluating their success in solving tightly coupled problems using a combination of simulation, hardware, and mixed reality experiments.