Understanding and modeling animal behavior is essential for studying collective motion, decision-making, and bio-inspired robotics. Yet, evaluating the accuracy of behavioral models still often relies on offline comparisons to static trajectory statistics. Here we introduce a reinforcement-learning-based framework that uses a biomimetic robotic fish (RoboFish) to evaluate computational models of live fish behavior through closed-loop interaction. We trained policies in simulation using four distinct fish models-a simple constant-follow baseline, two rule-based models, and a biologically grounded convolutional neural network model-and transferred these policies to the real RoboFish setup, where they interacted with live fish. Policies were trained to guide a simulated fish to goal locations, enabling us to quantify how the response of real fish differs from the simulated fish's response. We evaluate the fish models by quantifying the sim-to-real gaps, defined as the Wasserstein distance between simulated and real distributions of behavioral metrics such as goal-reaching performance, inter-individual distances, wall interactions, and alignment. The neural network-based fish model exhibited the smallest gap across goal-reaching performance and most other metrics, indicating higher behavioral fidelity than conventional rule-based models under this benchmark. More importantly, this separation shows that the proposed evaluation can quantitatively distinguish candidate models under matched closed-loop conditions. Our work demonstrates how learning-based robotic experiments can uncover deficiencies in behavioral models and provides a general framework for evaluating animal behavior models through embodied interaction.

翻译：理解与建模动物行为对于研究集体运动、决策制定及仿生机器人学至关重要。然而，行为模型精度的评估仍常依赖于与静态轨迹统计量的离线比较。本文引入一种基于强化学习的框架，利用仿生机器鱼（RoboFish）通过闭环交互评估活鱼行为的计算模型。我们使用四种不同的鱼类模型——基于简单恒定跟随的基线模型、两种基于规则的模型以及一种生物驱动的卷积神经网络模型——在仿真中训练策略，并将这些策略迁移至真实RoboFish系统，使其与活鱼交互。策略经过训练以引导模拟鱼到达目标位置，从而量化真实鱼响应与模拟鱼响应之间的差异。通过量化模拟-现实差距（定义为行为指标——如目标到达性能、个体间距离、墙壁交互及对齐度——的模拟分布与真实分布之间的Wasserstein距离）来评估鱼类模型。基于神经网络的鱼类模型在目标到达性能及多数其他指标上展现出最小的模拟-现实差距，表明在该基准下其行为逼真度优于传统基于规则的模型。更重要的是，这种差异表明所提出的评估方法能在匹配的闭环条件下定量区分候选模型。本研究展示了基于学习的机器人实验如何揭示行为模型缺陷，并为通过具身交互评估动物行为模型提供了通用框架。