Human Motion Prediction for Human-Robot Collaboration

This paper compares three human motion prediction methods — GMM-based clustering, TransFusion, and Graph-Mixer — evaluating their effectiveness in human-robot collaboration. These predictions are valuable for improving robot motion planning by anticipating human movements and enhancing safety and efficiency in dynamic environments.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-25
DOI
https://doi.org/10.5281/zenodo.14731153
Primary Topic
Vitamin D Research Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

Human Motion Prediction for Human-Robot Collaboration

Zenodo (CERN European Organization for Nuclear Research)
Vitamin D Research Studies
article

Human Motion Prediction for Human-Robot Collaboration

article en

Abstract

This paper compares three human motion prediction methods — GMM-based clustering, TransFusion, and Graph-Mixer — evaluating their effectiveness in human-robot collaboration. These predictions are valuable for improving robot motion planning by anticipating human movements and enhancing safety and efficiency in dynamic environments.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 51%
Vitamin D Research Studies
DOIOpen Access PDFOpenAlex
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Human Motion Prediction for Human-Robot Collaboration · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS