A Wearable Observatory Records How Work Moves

Robot-learning datasets often capture only part of a physical task: a camera view, body pose or hand contact. EgoExplorer's authors built a portable wearable system intended to observe those signals together during professional activities. The resulting 100-hour collection aligns egocentric video with reconstructed whole-body motion, detailed finger articulation, tactile observations and step-level descriptions. Its Calibrate-Complete-Constrain pipeline first fixes participant geometry and sensor placement, fills in unobserved joints with a temporal prior and then imposes measurement, continuity and geometry constraints. Against tracker-fitted references, the authors report a 71.9 percent reduction in body-point error relative to the strongest baseline, plus improvements in hand stability and contact estimation. Preliminary studies explore generation, conditioning and humanoid retargeting in simulation. This remains a research dataset and reconstruction method, not a guarantee that a robot can safely reproduce the recorded work.