Unsupervised early prediction of human reaching for human–robot collaboration in shared workspaces

This paper focuses on human–robot collaboration in industrial manipulation tasks that take place in a shared workspace. In this setting we wish to predict, as quickly as possible, the human’s reaching motion so that the robot can avoid interference while performing a complimentary task. Given an observed part of a human’s reaching motion, we thus wish to predict the remainder of the trajectory, and demonstrate that this is effective as a real-time input to the robot for human–robot collaboration tasks. We propose a two-layer framework of Gaussian Mixture Models and an unsupervised online learning algorithm that updates these models with newly-observed trajectories. Unlike previous work in this area which relies on supervised learning methods to build models of human motion, our approach requires no offline training or manual labeling. The main advantage of this unsupervised approach is that it can build models on-the-fly and adapt to new people and new motion styles as they emerge. We test our method on motion capture data from a human-human collaboration experiment to show the early prediction performance. We also present two human–robot workspace sharing experiments of varying difficulty where the robot predicts the human’s motion every 0.1 s. The experimental results suggest that our framework can use human motion predictions to decide on robot motions that avoid the human in real-time applications with high reliability.