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Dream Labs exits stealth with $90M, launches Dream-1 robot API
Dream Labs said Saturday it is leaving stealth with $90 million in seed funding and opening a public API for Dream-1, a model it says can control unseen two-arm robots, according to a company research post.
- New headline Was: “Dream-1”
- Our desk filed its story
- dreamlabs.ai is now reporting this
Dream Labs said Saturday it is coming out of stealth with $90 million in seed funding and making Dream-1 available through a public application programming interface, according to a company research post1.
The San Francisco startup described Dream-1 as a World Action Model that it says can control robots it has not seen, perform tasks it was not shown, and run at $3 an hour, or $2 an hour for a lighter Dream-1-flash variant1.
The company said a user can connect a new two-arm robot with parallel grippers to the API and start tasks without fine-tuning, demonstrations from that robot, or machine-learning expertise1.
Dream Labs said robotics has been stuck because a machine needs to be useful before people buy it, but usefulness has required data from that exact robot1.
It argued cheaper hardware and models that generalize without large embodiment-specific datasets can start a flywheel of more robots, more data and better models1.
The post said a capable two-arm setup can cost as little as about $2,000, that seven semi-humanoid robots now cost less than $15,000, and that Hugging Face’s $399 Microduck sold more than 10,000 units within four days of launch, citing Yahoo Tech1.
The team said it released DreamZero, a paper introducing World Action Models, in February 2026, and that post-training on 30 minutes of YAM robot data produced zero-shot transfer to unseen tasks, which it said used to require hundreds or thousands of hours of robot-specific data1.
Dream-1, it said, can output end-effector poses rather than only joint angles, so any two-arm parallel-gripper robot’s inverse-kinematics solver can turn those poses into joint commands1.
The company said the model uses a head camera and a wrist camera on each arm, and that predicting video and actions together lets it treat new bodies as pixels in a scene1.
It said Robocurve, which it described as a public-benefit corporation that measures robotics models, evaluated Dream-1 independently and found it cheaper and faster than other systems it compared, including models it named Opus 5.5, Gemini 4 Argon and GPT-6 Astra; those names and scores were not independently confirmed in this filing1.
When zero-shot is not enough, Dream Labs said Fine-tuning-as-a-Service can adapt the model; it showed examples of five minutes of data on partner robots, 77 minutes for a Dexmate system with dexterous hands, and 98 minutes for a YAM robot with swapped grippers1.
The company said Dream-1 today is a manipulation policy for two-arm robots with parallel grippers, with limits it tried to show, and that it wants to extend later to mobile manipulators and dexterous hands1.