Human–robot-collaboration requires robot to proactively and intelligently recognize the intention of human operator. Despite deep learning approaches have achieved certain results in performing feature learning and long-term temporal dependencies modeling, the motion prediction is still not desirable enough, which unavoidably compromises the accomplishment of tasks. Therefore, a hybrid recurrent neural network architecture is proposed for intention recognition to conduct the assembly tasks cooperatively. Specifically, the improved LSTM (ILSTM) and improved Bi-LSTM (IBi-LSTM) networks are first explored with state activation function and gate activation function to improve the network performance. The employment of the IBi-LSTM unit in the first layers of the hybrid architecture helps to learn the features effectively and fully from complex sequential data, and the LSTM-based cell in the last layer contributes to capturing the forward dependency. This hybrid network architecture can improve the prediction performance of intention recognition effectively. One experimental platform with the UR5 collaborative robot and human motion capture device is set up to test the performance of the proposed method. One filter, that is, the quartile-based amplitude limiting algorithm in sliding window, is designed to deal with the abnormal data of the spatiotemporal data, and thus, to improve the accuracy of network training and testing. The experimental results show that the hybrid network can predict the motion of human operator more precisely in collaborative workspace, compared with some representative deep learning methods.

中文翻译：

---

基于混合循环神经网络架构的人类机器人协作意图识别


人机协作要求机器人主动、智能地识别人类操作员的意图。尽管深度学习方法在执行特征学习和长期时间依赖性建模方面取得了一定的成果，但运动预测仍然不够理想，这不可避免地影响了任务的完成。因此，提出了一种混合循环神经网络架构用于意图识别以协作地执行组装任务。具体来说，首先探索了改进的LSTM（ILSTM）和改进的Bi-LSTM（IBi-LSTM）网络，利用状态激活函数和门激活函数来提高网络性能。在混合架构的第一层中使用 IBi-LSTM 单元有助于从复杂的序列数据中有效、充分地学习特征，最后一层中基于 LSTM 的单元有助于捕获前向依赖性。这种混合网络架构可以有效提高意图识别的预测性能。搭建了一个由 UR5 协作机器人和人体动作捕捉装置组成的实验平台来测试该方法的性能。设计一种滤波器，即滑动窗口中基于四分位数的限幅算法，来处理时空数据的异常数据，从而提高网络训练和测试的准确性。实验结果表明，与一些代表性的深度学习方法相比，混合网络可以更准确地预测协作工作空间中人类操作员的运动。