Abstract Effective and safe human-robot collaboration in assembly requires accurate prediction of human motion trajectory, given a sequence of past observations such that a robot can proactively provide assistance to improve operation efficiency while avoiding collision. This paper presents a deep learning-based method to parse visual observations of human actions in an assembly setting, and forecast the human operator's future motion trajectory for online robot action planning and execution. The method is built upon a recurrent neural network (RNN) that can learn the time-dependent mechanisms underlying the human motions. The effectiveness of the developed method is demonstrated for an engine assembly.

中文翻译：

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用于人机协作装配中运动轨迹预测的递归神经网络

摘要 装配中有效和安全的人机协作需要准确预测人体运动轨迹，给定一系列过去的观察结果，以便机器人可以主动提供帮助，提高操作效率，同时避免碰撞。本文提出了一种基于深度学习的方法来解析装配环境中人类动作的视觉观察，并预测人类操作员的未来运动轨迹，以进行在线机器人动作规划和执行。该方法建立在循环神经网络 (RNN) 之上，该网络可以学习人类运动背后的时间相关机制。所开发方法的有效性在发动机组件中得到了证明。