Definition
What is physical AI training data?
Physical AI training data is data that teaches models to perceive, reason about, and act in the physical world. It can include video, robot states, actions, teleoperation traces, human demonstrations, pose, tactile signals, environment metadata, and consent artifacts.
Comparison
| Modality | Examples | Why it matters |
|---|---|---|
| Egocentric video | Head-mounted task footage | Shows human interaction from first person |
| Teleoperation | Robot state plus action traces | Trains action-producing policies |
| Pose and IMU | Hands, head, body, motion | Adds structure beyond raw video |
| Metadata | Environment, object set, consent | Makes the dataset usable and auditable |
Why physical AI cannot rely only on web data
Physical AI systems differ from language models in one structural way: their learned behaviours must transfer to three-dimensional, contact-rich action spaces. RoboCat's manipulation agent consumed action-labelled visual experience from simulated and real robotic arms, which is a different evidence type from web text or static images [1]. Language-only planners also need physical grounding because large language models lack real-world experience for decisions inside a given embodiment [2]. Open X-Embodiment assembled robot-learning data from 22 different robots across 21 institutions because cross-robot generalisation depends on heterogeneous embodied trajectories [3]. DROID shows the same physical-data constraint at collection scale: its in-the-wild manipulation corpus spans 76k demonstrations, 350 interaction hours, 564 scenes, and 86 tasks [4].
[5]"However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment."
The constraint is not just quantitative; adding more web data does not create force feedback, failed grasp outcomes, robot state, action labels, or synchronized sensor streams.
What to specify before sourcing data
Before issuing a data collection brief, buyers should convert the model objective into concrete collection fields. Episode-count targets should be stated as accepted trajectories or accepted hours; BridgeData V2 reports 60,096 trajectories across 24 environments, while DROID's larger benchmark reports 76k demonstration trajectories, giving buyers useful scale anchors rather than vague "large dataset" language [6]. Modality requirements should follow the model's input head and delivery stack; the RLDS episode schema defines episodes, steps, observations, actions, rewards, discounts, and metadata as first-class fields [7]. Delivery-format requirements also matter because robotics logs often carry multiple timestamped channels; MCAP is designed as an open container for multimodal log data used in robotics applications [8]. Finally, success-rate thresholds and environment-diversity targets should be defined before collection begins, because LeRobot-style robotics datasets package videos, states, actions, and metadata for reproducible downstream training [9].
[10]"Metadata optional fields: episode_id: Unique identifier of the episode within the dataset."
Related pages
Use these to move from category-level context into specific task, dataset, format, and comparison detail.
External references and source context
- RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation
RoboCat used action-labelled visual experience spanning simulated and real robotic arms, making embodied action traces central to physical AI training data.
arXiv ↩ - Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Language models encode semantic knowledge but lack real-world experience for robotic decision-making within a physical embodiment.
arXiv ↩ - Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment assembled heterogeneous robotic manipulation data across robots, tasks, and institutions to improve cross-robot generalisation.
arXiv ↩ - DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
DROID documents the scale and diversity of in-the-wild robot manipulation data with 76k demonstrations, 350 interaction hours, 564 scenes, and 86 tasks.
arXiv ↩ - Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
SayCan explicitly states that language models lack real-world experience, making pure language/web-scale knowledge insufficient for robot decisions inside a physical embodiment.
arXiv ↩ - BridgeData V2: A Dataset for Robot Learning at Scale
BridgeData V2 provides a scale anchor for robot-learning data with 60,096 trajectories collected across 24 environments.
arXiv ↩ - RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning
RLDS is a standard, lossless ecosystem for recording, replaying, manipulating, annotating, and sharing sequential-decision datasets including demonstrations and imitation learning data.
arXiv ↩ - MCAP file format
MCAP is an open container format for timestamped multimodal log data and robotics pub/sub applications.
mcap.dev ↩ - LeRobot documentation
LeRobot documentation provides a robotics dataset ecosystem reference for videos, states, actions, and metadata used in downstream training workflows.
Hugging Face ↩ - RLDS: Reinforcement Learning Datasets
The RLDS schema defines episode and step fields that buyers can use as data-collection specification fields before sourcing physical AI data.
GitHub ↩ - RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
RT-2 describes co-fine-tuning vision-language-action models on robotic trajectory data alongside internet-scale vision-language tasks.
arXiv
FAQ
What is the difference between physical AI data and generic AI training data?
Generic AI training data can be text, images, audio, or labels. Physical AI data is tied to real or simulated action in the world, often requiring synchronized observations, states, actions, metadata, and rights documentation.
Why is consent important for physical AI data?
Physical AI data can include identifiable people, private homes, workplaces, or proprietary facilities. Consent artifacts and rights constraints help buyers defend downstream model use.
What is the first step to source physical AI data?
The first step is to write a spec: task, environment, modality, volume, format, rights, consent, budget, and acceptance criteria. truelabel turns that spec into a sourcing request that suppliers can respond to.
Does physical AI training data have to be robot data?
No. Human egocentric data, mocap, pose, and task footage can be valuable for physical AI, especially for world models, VLA models, and imitation-learning workflows. Some buyers also need robot teleoperation traces.
Looking for physical AI training data?
Specify modality, task, environment, rights, and delivery format. Truelabel matches you with vetted capture partners — every delivery includes consent artifacts and commercial licensing by default.
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