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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Physical AI models need data that connects what a robot senses and what it is asked to do with the actions it takes. For a manipulation policy, that often means synchronized workspace images, a task instruction, and robot actions or state. Broader capability depends not just on more examples, but on coverage of different tasks, objects, settings, and robot types. There is no established universal minimum dataset size or single recipe: the right data depends on the robot and its deployment task.
What a useful training example contains
A robot-learning example is most useful when it links the scene and task to the behavior that followed. At a minimum, that relationship may include an observation, task context, and action target. Additional sensors or state information can matter when the task requires them.
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Observation: what the robot can sense
Images or video show the robot’s surroundings and the objects it must interact with. The Open X-Embodiment RT-1-X repository illustrates a particular setup: an RGB image from a workspace camera. Its documented example does not include wrist-camera images or depth, a limitation of that interface rather than a rule for all physical-AI models. Open X-Embodiment’s repository provides an example workflow for inspecting data.
Other tasks can require other modalities. NVIDIA’s healthcare-focused Open-H-Embodiment dataset pairs video with kinematics, information relevant to its surgical-robotics and ultrasound domain. Sensors should be chosen for the task; a camera view that is adequate for one manipulation setup may not capture what another system needs.
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Instruction and context: what the robot should do
A task string tells the model what action is intended in the observed scene. RT-1-X uses a task string alongside the workspace image. Language can also convey concepts that are difficult to enumerate in fixed labels, but the instruction must be associated with examples of what carrying it out means physically.
Action and robot state: what happened
Demonstrations need an action target or a sequence of states and actions that connects the observation to behavior. In the RT-1-X example, the documented action space has seven variables describing gripper movement, including position, orientation, and gripper opening. That is one model’s interface, not a standard that every robot must use.
RT-2 uses a different representation: it serializes discretized robot actions as output tokens, including continuation or termination, position and rotation changes, and gripper state. Action labels must make sense for the robot that will execute them; different embodiments can have different sensors, movement conventions, and control spaces.
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A dataset can contain many episodes and still miss the conditions a deployed robot will face. Useful coverage may span different skills, objects, backgrounds, environments, task compositions, and robot embodiments. Multiple embodiments can broaden the situations represented, but data must still be mapped into compatible action and observation representations.
Open X-Embodiment combined data from 22 robot types and reported more than 500 skills, 150,000 tasks, and over one million episodes. Google DeepMind’s October 3, 2023 project account described participation by 33 academic lab partners. In its reported evaluation, RT-1-X achieved a 50% average success-rate improvement over corresponding independently developed methods across five labs and five commonly used robots. That is a result from those cross-robot experiments, not a guarantee that pooling data always improves a model. Google DeepMind’s account of Open X-Embodiment and RT-1-X describes the dataset and evaluation.
Different project counts should not be treated as a leaderboard. Episodes, trajectories, hours, tasks, and trials measure different things, and collections built for different domains are not directly comparable. None of these figures establishes a general number of examples needed to learn a real-world task.
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How web, robot, and simulation data can complement each other
Web-scale visual and language data can help a model recognize concepts and relate words to images. But knowing what an object or instruction means is not the same as knowing how to move a particular robot to achieve the task. Physical robot demonstrations provide that action grounding.
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RT-2 combined web and robotics data by co-fine-tuning a web-pretrained vision-language model with robot data; its actions were represented as model output tokens. Google DeepMind reported that its RT-1 demonstration dataset involved 13 robots and 17 months of collection, and that RT-2 experiments included more than 6,000 robotic trials. These are project-specific collection and experiment figures, not a general prescription for training. Google DeepMind’s RT-2 account explains the data mix and action representation.
Simulation can also contribute training experience. RT-2’s reported real-world evaluation used a model trained with both simulation and real data. That result does not establish a universally effective simulation-to-real ratio, or show that simulation alone is sufficient for reliable physical behavior.
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How to judge whether a dataset fits the deployment task
Before choosing or collecting data, compare it with the conditions and behavior the model must handle. These are practical checks, not a standardized scoring rubric.
- Modality and completeness: Check whether the collection includes relevant RGB views, video, depth where needed, kinematics or robot state, task text, and action labels—and whether those streams are synchronized.
- Task and scene coverage: Look for the skills, objects, environments, backgrounds, lighting, and combinations likely to appear at deployment.
- Embodiment coverage: Check which robot types and sensor placements are represented, how their action conventions differ, and whether the data can be mapped to the target robot.
- Collection source: Identify whether examples come from real-robot demonstrations, human teleoperation, automatic or sensor capture, simulation, web or human video, or a mix. Each source contributes different information; source alone does not establish fitness for a task.
- Evaluation coverage: Prefer evidence that tests held-out objects, tasks, backgrounds, or environments, as well as behavior on the actual robot where relevant. Success on familiar examples alone does not demonstrate generalization.
- Quality and rights: Review the specific dataset’s collection description, license, and intended-use terms before reuse. The available examples do not establish a universal quality or governance framework.
What published examples show—and what they do not
RT-2’s reported evaluation included previously unseen objects, backgrounds, and environments. Google DeepMind reported success rates of 32% to 62% on previously unseen scenarios and 90% on the Language Table simulation suite. These are results for the experiment and evaluation described by the authors, not expected performance levels for other models or tasks. The RT-2 account gives the project’s evaluation context.
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For a different domain, NVIDIA’s Open-H-Embodiment dataset card describes a healthcare-robotics collection of paired video and kinematics, with human, automatic or sensor-based, and synthetic collection methods. As of the dataset card’s February 2026 creation date, it reported 750 hours and 120,000 trajectories. It uses LeRobot v2.1 format, with MP4 video, Parquet kinematics, and JSON/JSONL metadata, and lists a CC-BY-4.0 license. These details describe that specialized collection; they do not imply that its modalities or license are suitable for unrelated deployments. The card is live, so its reported figures may change. NVIDIA’s Open-H-Embodiment dataset card contains the current dataset details.
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Open X-Embodiment offers another practical format example: its datasets are represented as sequences of episodes in RLDS, with a Colab workflow for visualizing examples and creating training and inference batches. A format can make data easier to inspect or use, but it does not by itself guarantee complete labels, broad coverage, or task performance. The repository’s documentation describes its format and workflow.
Building the data plan around the task
Start with the robot’s intended work, then collect examples that preserve the link between the conditions it perceives, the instruction it receives, and the actions that succeed. Add diversity where deployment is expected to vary, and evaluate on held-out conditions that reflect those variations. The cited projects demonstrate workable combinations of modalities and sources, but establish neither a universal minimum in hours or trajectories nor one best data recipe for every physical-AI system.
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