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Ensuring the safety of human workers in a collaborative environment withrobots is of utmost importance. Although accurate pose prediction models canhelp prevent collisions between human workers and robots, they are stillsusceptible to critical errors. In this study, we propose a novel approachcalled deep ensembles of temporal graph neural networks (DE-TGN) that not onlyaccurately forecast human motion but also provide a measure of predictionuncertainty. By leveraging deep ensembles and employing stochastic Monte-Carlodropout sampling, we construct a volumetric field representing a range ofpotential future human poses based on covariance ellipsoids. To validate ourframework, we conducted experiments using three motion capture datasetsincluding Human3.6M, and two human-robot interaction scenarios, achievingstate-of-the-art prediction error. Moreover, we discovered that deep ensemblesnot only enable us to quantify uncertainty but also improve the accuracy of ourpredictions.

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