Kareem A. Eltouny — accepted AI-conference papers

2024

DE-TGN: Uncertainty-Aware Human Motion Forecasting Using Deep Ensembles

Kareem A. Eltouny, Wansong Liu, Sibo Tian, Minghui Zheng, Xiao Liang

RA-L 2024

Ensuring the safety of human workers in a collaborative environment with robots is of utmost importance. Although accurate pose prediction models can help prevent collisions between human workers and robots, they are still susceptible to critical errors. In this study, we propose a novel approach ca

Cited by 19 Source ↗Scholar ↗

2022

Uncertainty-Assisted Image-Processing for Human-Robot Close Collaboration

Seyedomid Sajedi, Wansong Liu, Kareem A. Eltouny, Sara Behdad, Minghui Zheng, Xiao Liang

RA-L 2022

The safety of human workers has been the main concern in human-robot close collaboration. Along with rapidly developed artificial intelligence techniques, deep learning models using two-dimensional images have become feasible solutions for human motion detection. These models serve as “sensors” in t

Cited by 24 Source ↗Scholar ↗