Robotics data solutions built for Physical AI buyers.
Merit Data helps robot-learning, perception and embodied-AI teams source real-world data with the capture setup, metadata, quality controls and licensing records needed for serious procurement.
Designed mainly for US robotics teams, with project support for Europe and selected India-related collection or supplier coverage.
Different regions, same core trust problem.
US customers usually move fastest when the sample, pilot scope and commercial rights are clear. European buyers often ask more about consent, privacy and data transfer. India-related work is usually judged on cost, supplier verification and consistent delivery.
United States robotics teams
Best message: reduce engineering risk before a larger order.
- Matched raw samples before purchase
- Pilot-first delivery instead of vague volume claims
- Clear model-training license language
- Robot-learning fit: views, actions, states and metadata
European AI and robotics buyers
Best message: responsible sourcing and reviewable records.
- Consent and contributor release documentation
- PII review and project-level handling notes
- Data transfer and storage workflow discussion
- No unsupported certification claims
India-related collection or suppliers
Best message: cost-effective capacity with verification.
- Supplier and operator screening where required
- Clear task instruction and rejection rules
- Batch-level QC before client delivery
- Commercial terms tied to accepted data only
Choose by learning signal, not by generic footage.
Each solution can be sold as existing inventory, custom collection, annotation enrichment or a mixed pilot depending on what the customer needs.
Egocentric human demonstrations
First-person task videos for manipulation understanding, planning, VLA pretraining and world-model context.
- Best for early-stage teams needing broad human task behavior
- Can include task labels, scene type, object category and outcome
- Procurement proof: consent records, source notes and sample review
Egocentric + wrist-camera data
Synchronized head and wrist views for close-up hand-object interactions where pure egocentric footage is not enough.
- Best for grasping, placement, folding, sorting and tool-use tasks
- Can add hand pose, object interaction events and failure markers
- Procurement proof: camera setup, stream count and QC rejection rules
UMI, gripper and embodiment-relevant demonstrations
Data closer to downstream robot policy learning, including gripper-centered views and controlled manipulation episodes.
- Best for imitation learning and robot-ready manipulation programs
- Can be scoped by object set, task difficulty and environment diversity
- Procurement proof: protocol version, operator notes and pilot batch report
Teleoperation + robot state and action logs
Robot-executed episodes paired with observations, commands, states, timestamps and operator interventions.
- Best for teams that need more than video-only demonstrations
- Can include success, failure, intervention and recovery outcomes
- Procurement proof: data schema, sync logic and delivery manifest
Failure, intervention and recovery datasets
Episodes that show where policies break: slips, hesitation, wrong grasp, blocked movement, re-alignment and human correction.
- Best for post-training, evaluation and improving robustness
- Can be collected around specific model failure modes
- Procurement proof: failure taxonomy and acceptance criteria
Annotation and multimodal alignment
Structured enrichment for video and sensor datasets, including task events, object states, contact moments, QC notes and metadata.
- Best when customers already have data but need it training-ready
- Can support RGB, depth, LiDAR, IMU, action/state and timestamps
- Procurement proof: annotation guideline and reviewer audit trail