通过视觉注意力学习群体交互图，提升机器人集群行为模仿精度
Collective Behavior Clone with Visual Attention via Neural Interaction Graph Prediction
- 用图变分自编码器从轨迹数据中学习局部交互图
- 基于交互图与行为克隆，实现高精度群体控制策略学习
- 在真实视觉机器人集群上验证，适合未来群智机器人研究
本文提出一种集体行为克隆框架（CBC），用于学习群体系统中的交互机制与控制策略。给定群体系统的轨迹数据，我们采用图变分自编码器（GVAE）学习局部交互图，并结合该图与群体轨迹，通过行为克隆学习群体控制策略。为验证方法实用性，我们在一个真实的去中心化视觉机器人集群系统上部署该方法，基于学习到的交互图训练视觉注意力网络，实现在线邻居选择。实验结果表明，本方法在交互图预测和群体动作预测上均优于以往方法，准确率更高。该工作为未来群体机器人研究中理解交互机制与群集动力学提供了有效途径。代码与数据已公开。
原文摘要 · Abstract (English)
In this paper, we propose a framework, collective behavioral cloning (CBC), to learn the underlying interaction mechanism and control policy of a swarm system. Given the trajectory data of a swarm system, we propose a graph variational autoencoder (GVAE) to learn the local interaction graph. Based on the interaction graph and swarm trajectory, we use behavioral cloning to learn the control policy of the swarm system. To demonstrate the practicality of CBC, we deploy it on a real-world decentralized vision-based robot swarm system. A visual attention network is trained based on the learned interaction graph for online neighbor selection. Experimental results show that our method outperforms previous approaches in predicting both the interaction graph and swarm actions with higher accuracy. This work offers a promising approach for understanding interaction mechanisms and swarm dynamics in future swarm robotics research. Code and data are available.
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