Object Handover Prediction using Gaussian Processes clustered with Trajectory Classification
Abstract
A robotic system which approximates the user intention and appropriate complimentary motion is critical for successful human-robot interaction. %While the existing wearable sensors can monitor human movements in real-time, prediction of human movement is a significant challenge due to its highly non-linear motions optimised through the redundancy in the degrees of freedom. Here, we demonstrate robustness of the Gaussian Process (GP) clustered with a stochastic classification technique for trajectory prediction using an object handover scenario. By parametrising real 6D hand movements during human-human object handover using dual quaternions, variations of handover configurations were classified in real-time and then the remaining hand trajectory was predicted using the GP. The results highlights that our method can classify the handover configuration at an average of of the trajectory and the final hand configuration can be predicted within the normal variation of human movement. In conclusion, we demonstrate that GPs combined with a stochastic classification technique is a robust tool for proactively estimating human motions for human-robot interaction.
Cite
@article{arxiv.1707.02745,
title = {Object Handover Prediction using Gaussian Processes clustered with Trajectory Classification},
author = {Muriel Lang and Satoshi Endo and Oliver Dunkley and Sandra Hirche},
journal= {arXiv preprint arXiv:1707.02745},
year = {2017}
}