Abstract
For decades, robotics researchers have pursued various tasks for multi-robot systems, from cooperative manipulation to search and rescue. These tasks are multi-robot extensions of classical robotic tasks and often optimized on dimensions such as speed or efficiency. As robots transition from commercial and research settings into everyday environments, social task aims such as engagement or entertainment become increasingly relevant. This work presents a compelling multi-robot task, in which the main aim is to enthrall and interest. In this task, the goal is for a human to be drawn to move alongside and participate in a dynamic, expressive robot flock. Towards this aim, the research team created algorithms for robot movements and engaging interaction modes such as gestures and sound. The contributions are as follows: (1) a novel group navigation algorithm involving human and robot agents, (2) a gesture responsive algorithm for real-time, human-robot flocking interaction, (3) a weight mode characterization system for modifying flocking behavior, and (4) a method of encoding a choreographer's preferences inside a dynamic, adaptive, learned system. An experiment was performed to understand individual human behavior while interacting with the flock under three conditions: weight modes selected by a human choreographer, a learned model, or subset list. Results from the experiment showed that the perception of the experience was not influenced by the weight mode selection. This work elucidates how differing task aims such as engagement manifest in multi-robot system design and execution, and broadens the domain of multi-robot tasks.
Abstract (translated)
翻译： robot 研究者们多年来一直在追求多机器人系统的各种任务，从合作操作到搜索和救援。这些任务是经典机器人任务的扩展，通常在速度或效率等维度上进行优化。随着机器人从商业和研究环境向日常生活环境转变，社交任务（如参与或娱乐）变得越来越相关。这项工作展示了一个引人入胜的多机器人任务，其主要目标是为人类带来乐趣和兴趣。在这个任务中，目标是让一个人被吸引并参与到动态、表达性的机器人队中。为了实现这个目标，研究团队创建了涉及人类和机器人代理的新的机器人运动算法，以及诸如手势和声音等互动模式。贡献如下：（1）涉及人类和机器人代理的全新组队导航算法；（2）实时、人类机器人队紧耦合的姿势响应算法；（3）用于修改队形行为的权重模式特征系统；（4）在动态、自适应、学习系统内编码舞蹈编导偏好的方法。实验探讨了在三种条件下（由人类编舞者选择的重量模式，学习模型或子列表）与队进行交互的人类行为。实验结果表明，体验的感觉不受重量模式选择的影响。这项工作揭示了不同任务目标在多机器人系统设计和执行中的表现，并拓展了多机器人任务的领域。
URL
https://arxiv.org/abs/2404.00442
