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.
适合工作负载
首先,模型必须观察的身体行为.
当操作和设置明确时,VLA数据变得有用. 首先选择工作负载,然后定义该集合中的场景变化.
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.
- 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.
记录字段
保持每个剪辑与选择的文本连接.
一个一致的记录包裹允许数据团队检查场景匹配,将文件连接到元数据,并比较批量,而不需要从文件名中重建文本.
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
运行工作流程
一个简短的规则是发现,剪切,质量和交付.
WebScrapingAPI您的团队将关注场景定义以及样本记录是否适合预期模型工作流程.
- 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.
质量设计
质量意味着片段符合情景,记录解释了为什么.
收件团队可以将接受的记录与审查或拒绝的状态分开.
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.
交付设计
接收一次可检查的批量,或者保持现象供应活动.
选择一个单次集合,为定义的模型里程碑或一个重复的程序,在计划的序列上添加新的记录.
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
选择正确的道路
使用VLA路径,当物理环境定义剪辑是否属于.
更多的AI数据套件为团队提供了一般视频,准备评估包,管理收集和多元记录规划的明确路线.
路径最适合的定义对象下一步
Need a broader training, RAG, or agent data route? Explore Data for AI.
- 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
- Delivery options
- Volume, one-time or recurring cadence, destination, and support
What 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.