Key points
- Dream Labs raised $90 million in seed funding led by B Capital and Kindred Ventures.
- The company launched Dream-1, a foundation model that controls robots using video prediction.
- The public API allows developers to access the model for controlling bimanual robots.
Dream Labs has secured $90 million in seed funding to support its new universal robot foundation model, which aims to make industrial robots more intelligent and capable of performing multiple tasks through a new public application programming interface.
How it unfolded
- Dream Labs exited stealth mode and launched its Dream-1 foundation model alongside the new funding round.
- The company simultaneously made its public API available to all developers for immediate access.
- B Capital and Kindred Ventures jointly led the investment, with participation from several other venture firms.
Who is affected
Industrial robot owners and developers are the primary beneficiaries of this funding and technology launch. According to SiliconANGLE, there are more than five million industrial robots currently operating in factories and warehouses globally. These machines often perform single, repetitive tasks. The startup’s leadership argues that these robots lack the flexibility to learn new jobs or share knowledge with other machines, limiting their utility despite their widespread presence in modern manufacturing environments.
What happens next
The company plans to use the capital to expand its model’s capabilities and adoption. The Dream-1 model is designed to function as a universal brain for robots. It treats robotic skills as simple video prediction problems. This approach allows the system to control bimanual robots it has never seen, on tasks it was never shown, in environments it has never visited. The service is priced at three dollars per hour.
The background in plain language
Foundation models are large artificial intelligence systems trained on vast amounts of data to perform a wide variety of tasks, rather than being programmed for one specific function. In robotics, this means a single software system can guide different types of hardware. Video prediction involves the AI anticipating future frames in a video sequence, allowing it to understand motion and context. This enables robots to adapt to new situations without needing new code for every specific action or environment they encounter.
What to do and how to stay safe: Dream Labs
- Monitor the public API documentation to understand the technical requirements for integrating the model with existing robotic hardware.
- Evaluate current robotic workflows to identify tasks that could benefit from generalized intelligence rather than specialized programming.
- Review vendor support terms and pricing structures to assess the long-term cost implications of using cloud-based foundation models.
- Consult with engineering teams to determine if current hardware can interface with the new API without significant upgrades.
Step-by-step guide: Mobile Device Security for Small Business: Control Without Cost
General security guidance from the Circuit Observer newsroom. It is not confirmed advice from the organisations named in this story.
Frequently asked questions
How much funding did Dream Labs raise?
Dream Labs raised $90 million in a seed funding round.
What is the name of the robot foundation model?
The model is called Dream-1.
Who led the investment round for Dream Labs?
B Capital and Kindred Ventures jointly led the round.
