Robotics
Capture task sequences in the environments where they occur.
Organize manipulation, navigation, handoff, tool-use, and human–object interactions by task stage, viewpoint, setting, and visible conditions.
Scenario-led data for embodied systems
VLA Video Data for Physical AI
Build focused video collections around the actions, environments, conditions, viewpoints, and clip boundaries your physical-AI program needs.
- Scenario-led Action and context
- Clip-level Defined time boundaries
- Context-rich Viewpoint and conditions
- Fully operated Collection through delivery
Workload fit
Start with the physical behavior the model must observe.
VLA data becomes useful when the action and its setting are explicit. Choose the workload first, then define which scene variations belong in the collection.
Autonomous mobility
Build scenario coverage around road-user behavior and changing conditions.
Define maneuvers, intersections, road types, traffic states, weather, lighting, camera perspective, and the event window that makes the scene relevant.
World models
Preserve the sequence between state, action, and visible outcome.
Collect temporally coherent clips that retain environment, actor, object, action, and outcome context for simulation and representation-learning workflows.
Scenario brief
Turn a model need into searchable scene criteria.
The brief defines what must happen on screen and which variations matter. That gives collection, clipping, and review one shared target.
Build your scenario brief- 01Action
The behavior, task stage, or interaction that must be visible.
- 02Actor and object
The people, vehicles, tools, surfaces, or items involved in the event.
- 03Environment
The physical setting, layout, road type, workspace, or background context.
- 04Conditions
Lighting, weather, congestion, occlusion, motion, and other useful variation.
- 05Point of view
Egocentric, fixed, mobile, elevated, roadside, or another defined perspective.
- 06Time boundaries
The visible cue that starts the clip and the outcome that closes it.
Record design
Keep every clip connected to the context that selected it.
A consistent record envelope lets data teams inspect scene fit, join files to metadata, and compare batches without reconstructing context from filenames.
Focused video clip
Defined start and end around the visible action, with file properties kept beside the record.
- Clip identifier
- File reference
- Duration and orientation
Scenario meaning
The behavior, actor, object, task stage, and visible outcome represented in the selected window.
- Action sequence
- Actors and objects
- Start and completion cues
Scene conditions
Environment, point of view, lighting, weather, traffic, occlusion, and other selected dimensions.
- Environment class
- Viewpoint
- Condition values
Delivery metadata
Source reference, collection context, schema version, batch identity, and record-quality state.
- Source reference
- Batch and schema version
- Acceptance state
Operated workflow
One brief governs discovery, clipping, quality, and delivery.
WebScrapingAPI handles the collection workflow. Your team stays focused on the scenario definition and whether sample records fit the intended model workflow.
- 01DefineShared brief
Translate the workload into actions, environments, conditions, viewpoints, time boundaries, exclusions, and sample criteria.
- 02DiscoverWSA operates
Search the selected source universe for scenes that fit the approved context and event pattern.
- 03PrepareWSA operates
Set clip windows, assemble metadata, apply duplicate controls, and test records against the acceptance rules.
- 04DeliverWSA operates
Package accepted files and manifests, report batch quality states, and send them to the selected destination.
WebScrapingAPI ownsCollection, clip preparation, quality monitoring, source-change maintenance, and delivery.
Your team ownsModel objectives, scenario approval, sample acceptance, and downstream training or evaluation.
Quality design
Quality means the clip fits the scenario and the record explains why.
Acceptance rules are attached to the brief before volume expands. Every delivered state stays visible, so the receiving team can separate accepted records from review or rejected states.
Scenario fit
ContextThe required action, actor, object, environment, and selected conditions are visible in the clip.
Time-boundary fit
SequenceThe event begins and ends at the defined cues, with enough surrounding context to interpret the sequence.
Media integrity
FileThe delivered file can be opened, identified, and connected to its record and manifest.
Metadata completeness
RecordRequired context fields, source reference, batch identity, and schema version are present and readable.
Duplicate control
BatchRepeated files and near-identical scene records follow the program’s defined handling policy.
Delivery design
Receive an inspectable batch once, or keep the scenario supply active.
Choose a one-time collection for a defined model milestone or a recurring program that adds new records on a planned cadence.
Build a bounded scenario collection.
Use a fixed source scope, collection window, record schema, acceptance policy, and delivery event for training or evaluation.
- Versioned clip files
- Record index and manifest
- Batch quality summary
Refresh the scenario set on a planned cadence.
Retain the record contract while adding new files, condition coverage, or time periods to the selected secure destination.
- Consistent schema
- Duplicate and version policy
- Delivery-by-delivery states
Delivery envelope
- Media
- Video clips
- Index
- Structured metadata
- Control
- Manifest + schema version
- Destination
- Selected secure destination
Choose the right path
Use the VLA path when physical context defines whether a clip belongs.
The broader AI data suite gives teams clear routes for general video, ready-to-evaluate packages, managed collection, and multimodal record planning.
CaminhoMelhor ajusteObjeto de definiçãoPróximo passo
Need a broader training, RAG, or agent data route? Explore Data for AI.
Pricing orientation
Price the scenario program, not an abstract media count.
Scope follows the work needed to find, prepare, verify, organize, and deliver useful records for the defined physical-AI workload.
Discuss your VLA data brief- Scenario breadth
- Actions, environments, conditions, viewpoints, and exclusions
- Media work
- Source scope, collection window, clip preparation, and file organization
- Record depth
- Metadata fields, schema, manifests, and quality states
- Operating model
- Volume, one-time or recurring cadence, destination, and support
FAQ
Questions for a VLA video-data evaluation.
Use the answers to shape a scenario brief, sample review, delivery design, and operating boundary.
Talk to a data expertWhat is VLA video data?
VLA video data organizes visible actions and their surrounding scene context for vision-language-action and physical-AI workloads. A delivery can pair focused clips with point of view, environment, conditions, time boundaries, and descriptive metadata.
How do we define the right physical-AI scenarios?
Start with the action the model must observe, then define the actor or object, environment, operating conditions, point of view, clip start and end, and useful exclusions. WebScrapingAPI turns that brief into discovery and acceptance criteria.
What can each delivered record contain?
A record can connect the video clip to its source reference, action and scene description, point of view, environment, conditions, time boundaries, file properties, capture context, and schema version. The selected fields follow the program brief.
Can delivery be one-time or recurring?
Yes. A one-time delivery can support a defined training or evaluation window. A recurring program can add new scenario-matched records on a planned cadence while retaining the same record structure and acceptance rules.
What does WebScrapingAPI operate?
WebScrapingAPI operates source discovery and collection, clip preparation, metadata assembly, quality monitoring, source-change maintenance, duplicate controls, and delivery to the selected secure destination.
How is this different from general Video Data for AI Training?
The broader Video Data service supports multimodal model workloads across video, audio, transcripts, clips, and metadata. The VLA path adds a physical-scenario brief that makes action, environment, conditions, point of view, and time boundaries central to every record.
How is VLA video data delivered?
Video clips and their record index can be organized into versioned batches and sent to the selected secure destination. The delivery design covers file organization, manifest structure, schema version, cadence, and acceptance reporting.
What shapes pricing?
Pricing reflects scenario breadth, source scope, collection window, clip preparation, metadata depth, quality rules, volume, cadence, duplicate policy, file organization, delivery destination, and the operating support required.
Your first scenario
Turn one physical-AI scenario into a sample-ready data brief.
Share the action, environment, conditions, point of view, time boundaries, and preferred destination. We will map the brief to a collection and delivery design.