脚は腕より重要：ロボットの一人称視点による人間軌道予測における下半身姿勢の予測価値
Legs Over Arms: On the Predictive Value of Lower-Body Pose for Human Trajectory Prediction from Egocentric Robot Perception
ロボットの一人称視点から人間の骨格特徴を用いて軌道予測を行い、下半身の3Dキーポイントが予測精度を13%向上させることを示した研究。
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著者: Nhat Le, Daeun Song, Xuesu Xiao
分類: cs.RO
原文アブストラクト
Predicting human trajectory is crucial for social robot navigation in crowded environments. While most existing approaches treat human as point mass, we present a study on multi-agent trajectory prediction that leverages different human skeletal features for improved forecast accuracy. In particular, we systematically evaluate the predictive utility of 2D and 3D skeletal keypoints and derived biomechanical cues as additional inputs. Through a comprehensive study on the JRDB dataset and another new dataset for social navigation with 360-degree panoramic videos, we find that focusing on lower-body 3D keypoints yields a 13% reduction in Average Displacement Error and augmenting 3D keypoint inputs with corresponding biomechanical cues provides a further 1-4% improvement. Notably, the performance gain persists when using 2D keypoint inputs extracted from equirectangular panoramic images, indicating that monocular surround vision can capture informative cues for motion forecasting. Our finding that robots can forecast human movement efficiently by watching their legs provides actionable insights for designing sensing capabilities for social robot navigation.
