RoboFish: Mixed Shoals of Live Fish and Interactive Robots for the Analysis of Collective Behavior in Fish

鱼群系统是一种流行的研究集体行为的模型系统。在群体中，关于食物或捕食者的信息通过一波又一波的运动变化被分发给所有没有明确沟通渠道的成员，这些变化来自直接感知环境线索的动物。理解潜在的相互作用机制有助于阐明集体决策和领导过程。已经提出了各种数学模型，解释个体对哪些局部线索做出反应，以及这如何在集体层面上创造行为。这些模型在简化的计算机模拟中工作得很好，但无法对真实世界系统的行为做出具体预测。与将模型与预先记录的行为相适应不同，我们建议使用交互式机器人来验证、扩展和学习与自然系统环路中的集体运动模型。该提案的核心是，我们将使用强化学习框架来确定领导任务中的最佳机器人行为。在实时实验中，机器人将通过试错学习活鱼(孔雀鱼)的适当互动规则。这将加深我们对集体运动演化的理解，有助于澄清其功能，并允许对系统行为的稳健预测。我们的多机器人平台和学习策略可以应用于各种基于实验室的模型系统。通过开放源代码和硬件规范，我们促进了这一年轻方法的开发和传播。

Fish schooling is a popular model system for studying collective behavior. In a swarm, information on food or predators is distributed to all members without explicit communication channels through waves of movement changes, emanating from the animals that directly perceived the environmental cues. Understanding the underlying interaction mechanics elucidates collective group decision making and leadership processes. A variety of mathematical models have been proposed explaining upon which local cues individuals react and how this creates behavior on the collective level. These models work well in simplified computer simulations but fail to make specific predictions about the behavior of real-world systems. In contrast to fitting models to prerecorded behavior, we propose using interactive robots to validate, extend and learn models of collective motion in-loop with the natural system. Central to the proposal, we will use the Reinforcement Learning framework to identify optimal robotic behaviors in a leadership task. In real-time experiments, the robot will learn the appropriate interaction rules of live fish (guppies) by trial and error. This will deepen our understanding of the evolution of collective motion, will help clarifying its function and allow robust predictions of the system behavior. Our multi-robot platform and learning strategies can be applied to a variety of lab-based model systems. By opening the source code and hardware specifications we foster the development and dissemination of this young methodology.