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Humanoid robots must learn to balance, move, and interact with objects across an enormous range of situations, but the data available for this training has clear limits. Internet video shows diverse behavior but cannot capture precise physical states. Laboratory motion capture systems record accurate movement but usually cover only a narrow set of actions. This white paper examines HiPHI, a 617.5-hour whole-body human motion dataset captured with optical motion capture at sub-millimeter accuracy. It includes 245.7 hours of human-object interaction with synchronized object trajectories and meshes, and organizes coverage using FrameNet, a linguistic framework for human action. The paper also introduces a benchmark suite for measuring motion diversity and interaction grounding, and reports results from policies trained on the dataset and deployed on a physical Unitree G1 humanoid robot.
