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.

翻译：数十年来，机器人技术研究者一直致力于多机器人系统的各类任务——从协同操纵到搜救行动。这些任务本质上是经典机器人任务在多机器人领域的延伸，通常以速度或效率为优化维度。随着机器人从商业和研究场景逐步进入日常环境，以参与感或娱乐性为代表的社会性任务目标日益凸显。本文提出一种引人入胜的多机器人任务，其核心目标在于创造沉浸式互动体验。在该任务中，核心设计是要使人类被吸引着与动态、富有表现力的机器人群体同步移动并参与其中。为此，研究团队开发了机器人运动算法及手势、声音等交互模态。主要贡献包括：(1) 一种包含人类与机器人智能体的新型群体导航算法；(2) 用于实时人机群体交互的手势响应算法；(3) 用于调节群体编队行为的权重模式特征化系统；(4) 在动态自适应学习系统中编码编舞者偏好的方法。通过实验观察人类在三种权重模式（由人类编舞者选定、学习模型生成、子集列表）下与机器人群体互动时的个体行为。实验结果表明，权重模式的选择并未影响参与者的体验感知。这项工作阐明了参与感等差异化任务目标如何体现在多机器人系统设计与执行中，拓展了多机器人任务的研究领域。