Train Better Robots With Better Data
VisionLibra turns raw robot video, depth, point clouds, trajectories, and deployment logs into high-quality training and evaluation datasets. From annotation and failure mining to multimodal data collection and human QA — one data engine for Physical AI.
More Than Data Annotation
Traditional annotation labels what a camera sees. Physical AI data must also describe what the robot did, what happened, why it failed, and what it should do next. VisionLibra combines perception annotation, robot-state data, trajectories, human review, and evaluation into training-ready datasets.
Perception
Objects, masks, keypoints, depth, point clouds.
Action
Robot actions, trajectories, grasp states, task phases.
Outcome
Success, failure, intervention, recovery.
Intelligence
Failure taxonomy, root cause, preference, evaluation.
One data engine, six ways in
Start where your bottleneck is — perception labels, 3D geometry, robot episodes, failure mining, evaluation, or net-new collection. Same pipeline, same QA system underneath.
Computer Vision Annotation
High-quality human-reviewed annotation for robotics, autonomous systems, and spatial AI.
Supported tasks
RGB-D, Depth & 3D Annotation
Annotation for robots that reason about geometry, distance, and spatial relationships.
Supported tasks
Robot Trajectory & Action Annotation
Turn robot episodes into structured training data for imitation learning, manipulation, and Vision-Language-Action models.
Supported labels
Turn Failed Robot Runs Into Training Data
Your most valuable robot data may be the episodes where things went wrong. VisionLibra identifies, categorizes, and enriches failure episodes so engineering teams can spend less time manually reviewing logs and more time improving policies.
Perception Failure
Manipulation Failure
Planning Failure
Environment Failure
System Failure
Every failure record includes
Richer than bounding boxes
A single annotated manipulation episode carries perception, action, outcome, and root-cause intelligence — everything a retraining pipeline needs.
Know Whether Your New Policy Is Actually Better
Try Robot Evals free — upload a log, get a report in minutes →
VisionLibra helps robotics teams create repeatable evaluation datasets and score model performance across real-world scenarios.
Evaluation schemas and metrics are customized by application.
We bring the head-worn hardware. Your people just work.
This is a live collection program on an electronics assembly line: operators wear VisionLibra head-mounted capture rigs and simply do their jobs, while the rigs record first-person video and hardware-synchronized IMU data of every manipulation. That is the fastest path to production-grade egocentric datasets — no simulation gap, no staged demos.
- Wearable, hands-free capture — operators keep full mobility and normal pace
- Hardware-timestamped streams, uploadable at shift end with on-rig QC
- We design the program, supply the rigs, run collection, and deliver labeled data
Need Data You Don't Have Yet?
VisionLibra can help design and execute custom Physical AI data collection programs.
Supported collection
Modalities
Built for Multimodal Robotics Data
Vision
Geometry
Robot State
Motion
Interaction
Context
AI Speed. Human Judgment.
Every dataset moves through a staged pipeline: models do the heavy lifting, humans decide the hard cases, and validation gates the export.
Automated ingestion
Validate file structure, timestamps, sensor synchronization, and metadata.
AI pre-labeling
Detection, segmentation, tracking, and multimodal models create initial annotations.
Human annotation
Human reviewers correct ambiguous and complex cases.
QA review
Second-pass review based on project-specific quality rules.
Dataset validation
Schema validation, consistency checks, duplicate detection, and export validation.
Delivery
Model-ready dataset plus QA report.
Your Robot Data Stays Yours
- NDA available
- Customer retains ownership of data
- Project-specific access controls
- Isolated project workspaces
- Encryption in transit and at rest
- Data retention policy · optional deletion after project completion
- No customer data used for unrelated model training without permission
Wherever robots meet the real world
Warehouse Robotics
Picking, packing, navigation, safety.
Industrial Automation
Assembly, inspection, manipulation.
Humanoid Robotics
Manipulation, locomotion, task execution.
Retail Robotics
Inventory, shelf interaction, customer environments.
Autonomous Mobile Robots
Navigation, mapping, obstacle handling.
Drones
Perception, navigation, inspection.
Smart Home Robotics
Human interaction, object understanding.
Research
Academic robotics, VLA, embodied AI.
One Continuous Physical AI Loop
Already using VisionLibra hardware?
Capture synchronized spatial data directly from the VisionLibra stack and send selected episodes into the data workflow.
Using other hardware?
No problem. VisionLibra Data Services support third-party cameras, LiDAR, ROS, robot logs, and custom pipelines.
From raw terabytes to training signal
- 10,000 episodes
- 4.2 TB video
- RGB-D
- Logs
- Robot states
- 9,421 success episodes
- 579 failures
- 147 grasp failures · 96 perception failures
- 73 collisions · 61 recovery failures
- 202 additional edge cases
- Model-ready JSON / Parquet / ROS metadata
Start small, then scale on the same spec
Every collection program starts with a $25/hour pilot. What the pilot proves becomes the spec and unit price for production.
20-hour minimum ($500). One scenario, end to end, to prove quality and cost before you commit.
- 1 scenario, 1 site
- 20+ accepted hours of capture
- Collection protocol & QA report included
- Capture, AI pre-labeling, human QA
- Sample export in your format
- Production quote at the end
The pilot spec, run at volume with scheduled deliveries.
- Standard scenarios: $20–25 per accepted hour
- Custom scenario or your site: $30–40 / hour
- Annotation: +$10–25 / hour, by label depth
- 10,000+ hours: $20 / hour · larger volumes quoted
- Weekly or milestone deliveries
- QA report with every batch
Retainer: an ongoing data team for a deployed robot fleet.
- Continuous log ingestion
- Failure mining & edge cases
- Human review & evaluation
- Dataset versioning
- Dedicated project lead
License any of 23 recorded egocentric scenarios, available now.
- Sample clip for every dataset
- Research, commercial or exclusive license
- License pricing on request
- Annotation add-ons available
- No collection lead time
What you pay for
You pay for data that passes QA — not for camera time, retakes or our tooling.
| Service | Rate | Billing unit | What counts |
|---|---|---|---|
| Pilot | $25 / hour · 20-hour minimum ($500) | Per accepted hour | One scenario, collection protocol and QA report included |
| Collection — standard scenarios | $20–25 / hour | Per accepted hour of footage | Footage that passes QA against the agreed protocol; failed or off-protocol takes are not billed |
| Collection — custom scenario or site | $30–40 / hour | Per accepted hour of footage | Captured to your protocol, at a site you choose |
| Volume pricing | $20 / hour at 10,000+ h | Per accepted hour, standard scenarios | Larger programs are quoted individually |
| Annotation add-on | +$10–25 / hour | Per hour of footage annotated | Rate depends on label depth: action segments at the low end, hand keypoints and dense labels at the high end |
| Off-the-shelf datasets | On request | Per dataset license | The licensed scenario at the agreed volume |
| Robot trajectory & evaluation | Quoted | Per episode | Robot runs ingested, labeled and scored |
Prices in USD, excluding travel to customer-specified sites. Exact rates are confirmed in a written quote after a short scoping call.
What every delivery includes
Fixed in writing during the pilot, then held for every production batch.
| Data | |
|---|---|
| Video | MP4 (H.264 / H.265), resolution and frame rate as agreed in the collection protocol |
| Sensors | IMU, depth, robot state or audio where the rig records them, on the same clock as the video |
| Calibration | Camera intrinsics and extrinsics for multi-camera rigs |
| Structure | One folder per recording or episode, with a JSON manifest: scenario, operator ID, site, timestamps, take status |
| Labels | |
| Annotations | As specified in the schema: task / step segments, actions, hand keypoints, objects, trajectories, success / failure, language |
| Export | JSON, COCO, CSV, ROS bag, LeRobot / HDF5, or your own schema |
| Quality & handover | |
| QA report | Per batch: acceptance rate, rejected items and reasons, label-agreement checks, sync checks |
| Transfer | Secure cloud transfer, or a physical drive for large volumes |
| Privacy | Operator consent collected; face and screen blurring on request; NDA available |
Built for Robotics Research Too
VisionLibra supports universities, research labs, graduate researchers, and embodied AI teams with custom datasets and annotation workflows.
Possible projects
Common questions
Can VisionLibra annotate data collected with non-VisionLibra hardware?
Yes. We support third-party cameras, depth sensors, LiDAR, ROS systems, and custom robotics pipelines.
What types of robotics data do you support?
Images, video, RGB-D, depth maps, point clouds, LiDAR, robot state, trajectories, actions, telemetry, and multimodal datasets.
Do you support ROS bags?
Yes, project-dependent. Tell us your ROS version, topics, and dataset structure in the intake form and we'll confirm the pipeline.
Can you design the annotation schema?
Yes. Schema design is part of every project, and part of the pilot deliverable.
Can VisionLibra help identify robot failures automatically?
Yes. Failure Intelligence combines automated episode triage with human-reviewed taxonomy and root-cause annotation.
Do you provide human QA?
Yes. Every dataset passes human review and a second-pass QA stage based on project-specific quality rules.
Can you collect new robot data?
Yes, depending on task, location, hardware, and collection requirements. See custom dataset collection.
Can I start with a small pilot?
Yes. Collection projects start with a pilot at $25 per accepted hour with a 20-hour minimum ($500), which validates the protocol, quality and cost before scaling; annotation-only projects usually pilot on 100–500 episodes.
Who owns the annotated data?
The customer retains ownership of customer-provided data and resulting deliverables, subject to the project agreement.
Will my data be used to train other models?
Not without customer permission.
What file formats do you support?
Common inputs include JPG, PNG, MP4, depth maps, PCD, PLY, LAS, ROS bags, JSON, CSV, and HDF5. Exports include YOLO, COCO, Pascal VOC, JSON, CSV, PCD, Parquet, and custom schemas.
Your Robots Are Already Generating Training Data.
VisionLibra helps you find it, label it, evaluate it, and turn it into better models.
contact@visionlibra.com