仮想環境における安全な人間-ロボット協調のためのガウス過程による動作予測
Motion Prediction with Gaussian Processes for Safe Human-Robot Interaction in Virtual Environments
仮想現実での協調ロボット利用において、ガウス過程で人間の手の動きを予測し、視線情報と組み合わせて意図検出を行うことで、作業時間の短縮と安全性の向上を実現した。
著者: Stanley Mugisha, Vamsi Krishna Guda, Christine Chevallereau, Damien Chablat, Matteo Zoppi
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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\%.
