Transform raw robotic sensor streams, logs, and interaction traces into coherent, application-ready datasets, without heavy manual annotation.
Asynchronous and incomplete streams disrupt temporal coherence, lowering model accuracy.
Lack of detailed annotations for actions and environments slows training and limits generalization.
Misalignment across vision, motion, and language data hinders transferable learning and sim-to-real performance.
.avif)
Temporal-Multimodal Alignment & Synchronization Engine
Interaction Graph Construction & Semantic Event Recognition
Label Completion, Augmentation & Schema Harmonization
Physics Aware & Reality Grounded Data Augmentation
Multimodal Knowledge Graph Creation
Comprehensive curation: precise temporal sync, rich interaction labels, seamless multimodal fusion.
Rich egocentric video data; less focus on multimodal synchronization.
Strong in vision-language pre-training; limited temporal alignment.
Useful embodied AI benchmark; moderate multimodal coverage.
