Human-Robot collaboration (HRC) is an important topic for manufacturing and household robotics. It is very challenging to ensure both efficiency and safety in HRC. This paper presents an HRC pipeline that generates efficient and collision-free robot trajectories based on predictions of the human arm and hand (AH) motions. We train a recurrent neural network for AH trajectory prediction based on observed initial trajectory segments. To increase the accuracy of target estimation at an early stage, the observed and the predicted hand palm trajectory are combined to predict the current AH motion target using Gaussian Mixture Models (GMMs). An optimization-based trajectory generation algorithm is proposed to ensure the safety of the human while collaborating with the robot. The proposed system is validated in a shared-workspace scenario with human pick-and-place motions. The task can be safely and efficiently completed. The results demonstrate that our proposed pipeline can predict the human AH trajectory and estimate the motion target intended by the human accurately and early.

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.