Object Handover Prediction using Gaussian Processes clustered with Trajectory Classification

TL;DR

This paper presents a method combining Gaussian Processes and stochastic classification to predict human hand trajectories during object handover, enhancing human-robot interaction by accurately estimating movements in real-time.

Contribution

The novel integration of Gaussian Processes with stochastic classification for real-time trajectory prediction in human-robot handover scenarios.

Findings

Classifies handover configurations at 43.4% of the trajectory

Predicts final hand configuration within normal human variation

Demonstrates robustness of the combined approach in real-time prediction

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 $43.4\%$ 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.