Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments
This work addresses safety and efficiency challenges for users of collaborative robots in virtual reality applications, though it appears incremental as it builds on existing prediction methods with specific enhancements.
The paper tackled the problem of improving efficiency and safety in human-robot interaction for virtual environments by using Gaussian process models to predict human hand motion and detect intentions, resulting in improvements such as a 3% increase in robot time and 17% in safety with hand motion alone.
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\%.