用联邦学习让多架无人机协同识别人类指令，解决遮挡问题。
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
- 基于双层LSTM与密集连接网络，结合联邦学习实现分布式训练。
- 真实机器人实验准确率超96%，有效应对操作员被遮挡场景。
- 适合多无人机协作、隐私敏感的智能人机交互系统应用。
人机交互（HRI）是研究热点，但复杂指令分类仍面临挑战，常限制实际应用。现有方法多采用神经网络检测动作，但在使用无人飞行器（UAV）时，因移动导致操作者常脱离视野，遮挡问题尤为突出。此外，多机器人场景下的分布式训练也缺乏有效方案。本文提出一种基于双层LSTM深度神经网络与三个密集连接层的动作识别与控制方法，并嵌入联邦学习（FL），实现多架无人机的分布式训练。该方法无需依赖云端或集中存储，可在本地完成模型更新。实机实验表明，该多机器人系统在遮挡情况下仍可实现超过96%的识别准确率。
原文摘要 · Abstract (English)
The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.
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