Humans use collaborative robots as tools for accomplishing various tasks. The interaction between humans and robots happens in tight shared workspaces. However, these machines must be safe to operate alongside humans to minimize the risk of accidental collisions. Ensuring safety imposes many constraints, such as reduced torque and velocity limits during operation, thus increasing the time to accomplish many tasks. However, for applications such as using collaborative robots as haptic interfaces with intermittent contacts for virtual reality applications, speed limitations result in poor user experiences. This research aims to improve the efficiency of a collaborative robot while improving the safety of the human user. We used Gaussian process models to predict human hand motion and developed strategies for human intention detection based on hand motion and gaze to improve the time for the robot and human security in a virtual environment. We then studied the effect of prediction. Results from comparisons show that the prediction models improved the robot time by 3\% and safety by 17\%. When used alongside gaze, prediction with Gaussian process models resulted in an improvement of the robot time by 2\% and the safety by 13\%.

翻译：人类将协作机器人作为完成各种任务的工具。人机交互发生在紧密的共享工作空间中。然而，这些机器必须与人类安全协同操作，以最大程度降低意外碰撞的风险。确保安全会施加许多约束，例如在操作过程中降低扭矩和速度限制，从而增加了完成许多任务的时间。但在将协作机器人用作虚拟现实应用中具有间断接触的触觉接口等场景时，速度限制会导致用户体验不佳。本研究旨在提高协作机器人的效率，同时提升人类用户的安全性。我们采用高斯过程模型预测人类手部运动，并基于手部运动和视线开发了人类意图检测策略，以在虚拟环境中提升机器人和人类的安全时间。随后我们研究了预测效果。比较结果表明，预测模型使机器人时间提升了3%，安全性提升了17%。当结合视线使用时，基于高斯过程模型的预测使机器人时间提升了2%，安全性提升了13%。