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Can forecasting scene and hand motion in 3D make dexterous robot policies more reliable? This episode explains what PointWAM's geometric representation achieves, which experiments support its gains, and where the evidence remains narrow. We examine three findings: the 11.7-percentage-point advantage over GR00T N1.6 on the paper's ten-task DexJoCo protocol; the controlled improvement from adding scene-trajectory supervision; and the surprisingly large advantage of anatomical hand keypoints over sampled hand surfaces. The discussion follows the full pipeline from EgoDex and VITRA human demonstrations to point-trajectory forecasting and learned robot-action retargeting. Evaluation spans simulated Franka–Allegro manipulation, a difficult parallel-jaw-gripper benchmark, and real OpenArm experiments with Inspire hands and stereo vision. The central limitation is scope: real-robot testing covers two tasks with small trial counts, while precision-task completion remains low outside the main benchmark. We also unpack a confounded 2D-versus-3D ablation, checkpoint selection, preprocessing costs, and the distinction between predictive supervision and planning with imagined futures. Is PointWAM's most important contribution a better world model—or a better interface between human motion and robot control?
