PHILADELPHIA, PA, April 5, 2026 — DreamVu today released PRISM, a 270,000-sample multi-view video dataset collected across five real supermarkets for training and evaluating vision-language models on embodied AI tasks. Fine-tuning on PRISM reduces average error rates by 66.6% and cuts embodied reasoning errors by a factor of five compared to general-purpose baselines, across 20 capability probes evaluated in the accompanying paper.
Existing training datasets typically address spatial, physical, or action-level reasoning in isolation. PRISM covers all three in a single deployment domain, captured from both egocentric (worker-worn) and wide-angle 360° overhead cameras using Alia360.
"The core finding is that domain-specific fine-tuning on data covering spatial, physical, and action reasoning together produces gains that general-corpus scaling does not. We're releasing the paper so the research community can build on it."
— Rajat Aggarwal, Co-Founder and CTO, DreamVu
About DreamVu
DreamVu designs and builds patented 360° 3D cameras for robots, and the software that runs on them. The company is headquartered in Philadelphia, PA, with offices in Palo Alto, CA and R&D in Hyderabad, India.
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