Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments

TL;DR

This paper presents a Gaussian process-based approach to predict human hand motion and detect intention, enhancing safety and efficiency in human-robot interactions within virtual environments.

Contribution

It introduces a novel application of Gaussian processes for human motion prediction and intention detection to improve safety and speed in collaborative robot tasks.

Findings

Prediction models improved robot operation time by 3%.

Safety increased by 17% with motion prediction.

Gaze integration further enhanced safety by 13%.

Abstract

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\%.