  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\%.
