# Object Handover Prediction using Gaussian Processes clustered with Trajectory Classification

arXiv: https://arxiv.org/abs/1707.02745 · HTML: https://mlsanity.com/paper/1707.02745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [AnyPose: Anytime 3D Human Pose Forecasting via Neural Ordinary Differential Equations](https://mlsanity.com/paper/2309.04840/) (2023)

## Abstract (arXiv)

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.