We are building the largesthuman centric omni-modal datasetfor embodied ai

• Multimodal Sensor Fusion and State Estimation
• Multi-Scale Hierarchical Interpretability
• Sim-to-Real Alignment and Zero-Shot Transfer
• Cross-Ontology Compatibility and Morphological Abstraction

Build whole-body intelligence with context—full-body motion aligned with video in large, unrestricted spaces.

The HiPHI-MOV Dataset is a human-centric, high-fidelity multimodal corpus specifically engineered for the development of robust locomotion and whole-body loco-manipulation policies. It includes full-body motion capture, tracking of interacting objects, egocentric RGB-D visual data, third-person RGB-D visual data. HiPHI-MOV provides a synchronized data stream that co-registers ground-truth, full-body kinematic trajectories captured via high-frequency motion capture with ego-centric and exo-centric visual observations. This structured hierarchy enables the modeling of complex robotic behaviors, ranging from low-level motor primitives (joint-space dynamics) to high-level environmental aff ordances (scene-contextual navigation).

Teach robot hands true dexterity—millimeter finger motion plus pressure, captured for industrial precision.

The HiPHI-OM Dataset is a human-centric, high-fidelity, omni-modal repository acquired within a highly instrumented laboratory environment. It includes fullbody and fine grained hand motion capture, hand level tactile sensing, precise tracking of interacting objects, egocentric RGB-D visual data, third person RGB-D visual data, audio and temperature measurements. By utilizing synchronized, high-precision sensor arrays, HiPHI-OM provides ground truth level data for anthropocentric modeling with minimal aleatoric uncertainty. The dataset is designed to be ontology-agnostic, allowing for the decoupling of raw sensor data from specific semantic frameworks to maximize cross-domain generalization and longitudinal utility. Morphologically, the dataset supports a hierarchical structure, encompassing both micro-level kinematic primitives and meso-level sequential task planning.

• Human-Centered Hierarchical Modeling
• Sub-millimeter Spatiotemporal Synchronization
• Multimodal, High-Dimensional Signal Registration
• Cross-Ontology Compatibility and Morphological Mapping

• Ecological Validity and Scene Generalization
• Capture of Unconstrained Behavioral Dynamics
• High-Throughput Distributed Acquisition
• Cross-Ontology Annotation and Compliance Pipeline

Train for reality, not the lab—natural human behavior across diverse real-world environments.

The 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. Whenintegrated with the HiPHI-OM high-precision dataset, it provides a comprehensive cross-domain corpus spanning diverse operational scenarios and sensory modalities.