ITW+

The In-the-Wild (ITW+) Dataset constitutes a human-centric, life-scale, diverse, open-world, multimodal repository of stochastic real-world scenarios designed to advance humanoid robotics and embodied intelligence (EI). It includes sparse-body motion capture sensors and egocentric RGB-D visual data. Departing from traditional laboratory-constrained acquisition, ITW+ captures ecologically valid human behaviors and interaction dynamics within unconstrained environments. By integrating high-variance environmental noise and long-tail edge cases into the training distribution, ITW+ facilitates the generalization of laboratory-optimized algorithms toward industrial deployment. When integrated with the HiPHI-OM dataset, it provides a comprehensive cross-domain corpus spanning diverse operational scenarios and sensory modalities.

ITW+ is built for MultiModal Machine Learning: every episode pairs egocentric vision with time-aligned motion, depth, audio, and tactile streams, so models can learn joint representations across sensing modalities rather than from any single signal in isolation. The new ITW+ Motion and ITW+ Tactile acquisition modalities extend this with full-body skeleton capture and hand-pressure sensing collected in the same unconstrained scenarios.