Biohybrid systems in which robotic lures interact with animals have become compelling tools for probing and identifying the mechanisms underlying collective animal behavior. One key challenge lies in the transfer of social interaction models from simulations to reality, using robotics to validate the modeling hypotheses. This challenge arises in bridging what we term the "biomimicry gap", which is caused by imperfect robotic replicas, communication cues and physics constrains not incorporated in the simulations that may elicit unrealistic behavioral responses in animals. In this work, we used a biomimetic lure of a rummy-nose tetra fish (Hemigrammus rhodostomus) and a neural network (NN) model for generating biomimetic social interactions. Through experiments with a biohybrid pair comprising a fish and the robotic lure, a pair of real fish, and simulations of pairs of fish, we demonstrate that our biohybrid system generates high-fidelity social interactions mirroring those of genuine fish pairs. Our analyses highlight that: 1) the lure and NN maintain minimal deviation in real-world interactions compared to simulations and fish-only experiments, 2) our NN controls the robot efficiently in real-time, and 3) a comprehensive validation is crucial to bridge the biomimicry gap, ensuring realistic biohybrid systems.

翻译：生物混合系统（即机器人诱饵与动物交互的系统）已成为探究和识别群体动物行为机制的有力工具。其中一个关键挑战在于将社会交互模型从模拟环境迁移至现实世界，并利用机器人技术验证建模假设。这一挑战源于我们所谓的“仿生差距”——由机器人复制品的非完美性、通信线索及模拟中未纳入的物理约束共同导致，这些因素可能引发动物产生不切实际的行为反应。本研究采用一种仿生诱饵（仿制红鼻剪刀鱼Hemigrammus rhodostomus）和生成仿生社会交互的神经网络模型。通过包含鱼与机器人诱饵的生物混合对、真实鱼对以及模拟鱼对的实验，我们证明该生物混合系统能生成与真实鱼对高度相似的社会交互行为。分析表明：1）与模拟环境和纯鱼实验相比，诱饵与神经网络在现实交互中保持最小偏差；2）我们的神经网络能高效实时控制机器人；3）全面验证对弥合仿生差距、确保生物混合系统的真实性至关重要。