Research
Research Highlights
DreamVu Research is bridging the knowledge gap for physical AI, creating the high-fidelity datasets required for the next generation of humanoid robots and embodied agents.
NEW · MAY 2026
SABER PAPER
SABER: A Scalable Action-Based Embodied Dataset for Real-World VLA Adaptation.
The first high-fidelity retail robotics action dataset built from natural human behavior, not teleoperation.
The Impact
Domain-specific robot deployment is fundamentally a data problem. SABER demonstrates that human video — systematically captured and retargeted — is a scalable foundation for robot adaptation, achieving 2.19X improvement over baselines.
44.8K
Training Samples
100+
Hours Captured
91%
Fridge Task Success Rate
PUBLISHED · MARCH 2026
PRISM PAPER
PRISM: Unifying physical AI knowledge across space, physics, and embodied action.
Bridging the reasoning-action gap for VLMs in real-world environments.
The Impact
PRISM provides the structural bridge between visual understanding and physical execution, reducing average error rates by 66.6% across embodied reasoning tasks.
66.6%
Error reduction
270K
Samples
5×
Reasoning Performance Gain