A new, densely annotated 3D-text dataset called 3D-GRAND can help train embodied AI, like household robots, to connect language to 3D spaces. The study, led by University of Michigan researchers, was presented at the Computer Vision and Pattern Recognition (CVPR) Conference in Nashville, Tennessee on June 15, and published on the arXiv preprint server.
AI generates data to help embodied agents ground language to 3D world
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