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Dream Labs has launched Dream-1, a World Action Model available through a public API that it says can control previously unseen bimanual robots with parallel grippers without robot-specific fine-tuning or demonstrations. The company emerged from stealth with $90 million in seed funding and positions the system as a way to make newly deployed, lower-cost robots useful before they have accumulated their own training data. Dream-1 predicts end-effector poses—gripper positions, orientations, and open or closed states—rather than only robot-specific joint angles. A robot’s inverse-kinematics solver converts those poses into joint commands. The model uses a head camera and wrist cameras, and Dream Labs says its video-and-action training enables it to generalize across robots whose bodies and grippers differ. The company reported tests on multiple bimanual parallel-gripper systems, including the 18-year-old PR2 robot. Robocurve independently evaluated Dream-1 before launch, according to Dream Labs. Dream Labs says the model costs $3 per hour and Dream-1-flash costs $2 per hour; each API call returns 1.6 seconds of actions. For robots where zero-shot performance is insufficient, its Fine-tuning-as-a-Service adapts the model using robot-specific data. Dream Labs reports five minutes of data improved results for two partner robots, while other configurations required 77 to 98 minutes.