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00:00 / 01:59 Launch video Give Dream-1 a new bimanual robot with parallel grippers, connect it to our API, and it can start performing tasks out of the box.
No fine-tuning, no demonstrations from that robot, and no machine learning expertise required.
This is an important step toward general-purpose robot intelligence. Today, Dream Labs is also coming out of stealth with $90M in seed funding and making Dream-1 available to people all around the world through a public API. 01Breaking the chicken-and-egg problem Robotics' chicken-and-egg problem.
For decades, robotics has been stuck in a chicken-and-egg problem: a robot needs to be useful before people want to buy it, but making it useful requires data from that exact robot, and that data only exists once enough people start using it.
Dream-1 is our first step toward breaking that loop.
Self-driving found a way around this cold-start problem.
Cars were useful from day one with zero autonomy, so millions of people drove them every day and generated the data needed to train increasingly capable models.
Most other robots never had that advantage. Two things are changing that. First, robot hardware is getting cheap enough to reach far more people.
A capable bimanual arm setup can now cost as little as $2k1. Second, a robot may no longer need a large dataset of its own before it can be useful. If a model can generalize zero-shot to unseen environments, tasks, and even robots, a new robot can start with useful capabilities.
Put those together and the flywheel can finally start. Cheaper hardware puts more robots into the world. Generalizable models make those robots useful earlier. Useful robots generate more data, and that data makes the models better for the next robot.
02The Cambrian explosion of cheap, affordable robots Dream Labs enabled robots. Robot hardware is getting cheap faster than anyone expected. See the robots above, where 7 semi-humanoid robots cost less than $15k, a price tier that barely existed a year ago. At the far end of the price curve, Hugging Face's $399 Microduck sold more than 10,000 units within 4 days of its launch in late August2. We believe the robotics take-off will start with robots like these. $2,000a bimanual arm setupA capable two-arm setup, as of today. 7semi-humanoids under $15kWheels, two arms, parallel grippers; a tier that barely existed a year ago. $399Hugging Face MicroduckLaunched late August. 10,000+Microducks in 4 daysSold within four days of launch.2 That's because their buyers are consumers, prosumers, and small teams, not enterprises.
An enterprise buyer is making a spreadsheet decision. If the robot doesn't beat a person on cost, speed, or reliability, it doesn't get bought; that's a high bar to clear. Consumers are different.
10,000+ people just paid $399 within 4 days for a robot whose main job is to be delightful and cute: it walks, waddles, and roller-skates. Somewhere between that and a fully capable home robot is a sweet spot, a robot affordable enough to be an easy yes and useful enough to earn its place in the home. Once robots hit that point, we think adoption starts to compound, and every robot in a home becomes a source of new training data. Imagine two Microducks folding your laundry, using their beaks as grippers. 03The result that started Dream Labs In February 2026, our team released DreamZero, the paper that introduced World Action Models3. Since then, the community has adopted WAMs as one of the most promising paradigms in robot learning. At that time, the most surprising result for us was when after post-training DreamZero on just 30 minutes of YAM robot data, we saw it generalize zero-shot to tasks it had never seen. That kind of generalization used to take hundreds or even thousands of hours of embodiment-specific data. If 30 minutes of data from a given robot was enough to transfer general-purpose task knowledge to that robot, breaking the chicken-and-egg loop no longer looked impossible. Combined with the hardware wave above, the timing felt right. That's when we decided to start Dream Labs. After six months of building, we're excited to share a new research breakthrough that was only made possible through world action models: early signs of zero-shot embodiment generalization. 04Introducing Dream-1 Dream-1 is our first World Action Model available through a public API that can be used all around the world. Out of the box, it can control unseen robots for unseen tasks in unseen environments. Anvil RoboticsCaribbeanSort the candy into containers 1 of 2 BitRobotShenzhen, ChinaPull out a tissue 1 of 4 CortexSingaporeStack the white cup on top of the pink cup 1 of 2 EastworldKL, MalaysiaPlace the apple in the brown box 1 rollout Hugging FaceParis, FrancePick up the red cube and put it in the green container 1 rollout KAISTSeoul, KoreaFold the box 1 rollout XDOFJakarta, IndonesiaFind the marker and pick it up 1 rollout RoboCurveSan Francisco, USAStand all three bottles upright 1 of 4 Scale AISan Francisco, USAStack the cubes into a single tower 1 of 9 Robo Inc.Los Angeles, USAMove chips to tray 1 rollout MeckaNew York, USALift a test tube from the loose rack 1 rollout AgiBotShanghai, ChinaPick apple to plate 1 rollout 1–13 of 13 Unseen Tasks Dream-1 was able to do.
Autonomous.
8x speed at the beta testers’ sites, 4x in-house. 04.1Get started in minutes We want generalizable robotics to be accessible to anyone with a robot, not just large labs. So we designed Dream-1 to be as close to plug-and-play as possible. Connect your robot, point it at the API, and run. No machine learning background is needed to get started. Every week, we run internal hackathons to keep time-to-deploy as short as possible. Getting started tutorial import dream client = dream.DreamClient( api_key=<API_KEY>, ) client.policy({"checkpoint": "dreamlabs/dream-1"}) client.set_observation(observation) action = client.get_action() client.close() Condensed client side code 04.2Zero-shot on robots it has never seen Dream-1 can zero-shot generalize to many bimanual, parallel-gripper robots. Here are some we've tested. It can even control the original PR2, a general-purpose bimanual research robot released 18 years ago. Note that no embodiment-specific data was used to get these rollouts. Unseen Robots Dream-1 was able to control. 4x speed on real robots, 3x in simulation. This is one of the first reported zero-shot generalizations to completely unseen robot embodiments.4 If you have a bimanual parallel-gripper robot, try prompting Dream-1 and let us know how it goes. Zero-shot performance depends partly on your robot's inverse kinematics (IK) solver, so we've written a guide to help you get the most out of it.
IK guide.
Performance also varies across robots.
Below, we break down zero-shot performance in simulation and on real robots.
Dream-1 zero-shot performance on 27 unseen robots in simulation and 4 real robots, relative to YAM, the robot Dream-1 was actually trained on. The real robots ran earlier Dream-1 checkpoints that never trained on them, each compared with the same checkpoint on YAM; OpenYAM was tested on unseen tasks only. Simulated evaluations are conducted by doing 300 rollouts per embodiment: 150 rollouts of 50 tasks quantifying language following and 150 rollouts of 150 tasks quantifying unseen task generalization.
Below, we explain how zero-shot embodiment generalization works and where it falls short.
We then show how one-shot embodiment learning closes that gap.
04.3How zero-shot embodiment generalization works Two ways to control a robot: joints or end-effectors.
Robot policies commonly output joint angles, which ties them to one specific robot's body.
Dream-1 can do the same for the embodiments it supports natively, but it can also predict end-effector (EEF) poses: where each gripper should be and whether it should be open or closed.
Every robot with two arms and parallel grippers shares that EEF action space, and its own IK solver turns the poses into joint commands.
That shared interface gives Dream-1 a common way to command robots it has never seen, but on its own it isn't enough to make zero-shot transfer work.
Why does this work?
Dream-1 sees the world through three cameras: a head view and a wrist view on each arm. Prior work on the Universal Manipulation Interface (UMI) showed that policies can transfer zero-shot across robot arms when every arm uses the same gripper and the policy relies only on wrist cameras5.
From the wrist's point of view, the robots look identical.
But that shortcut has a hard limit. It only works when the gripper hardware matches exactly, and it gives up the head camera's view of the whole scene.
World Action Models remove that limit. A WAM learns to predict video and actions together, so controlling a robot and imagining what that robot will do are the same problem.
And every robot shows up the same way: as pixels.
The head camera sees a UMI gripper, a two-finger gripper or an arm Dream-1 has never met as nothing more than shapes moving through a scene, which makes video the one representation every robot type shares.
If Dream-1 can predict what a robot will look like as it completes a task, it can produce the actions that get it there. That’s why it can control robots with grippers that look nothing like UMI’s, and even entirely new bodies seen through the head camera.