Embodied AI data collection vendors: a directory for robotics teams

Embodied AI models learn from physical interaction, not just text or static images. The training signal comes from sensor streams, operator demonstrations, robot states, and the outcomes of actions taken in the real world. That makes the data collection pipeline genuinely different from NLP or standard computer vision work, and it makes vendor selection consequential in ways that go beyond price per label.

The wrong vendor can introduce synchronization gaps between sensor streams, apply annotation taxonomies that don’t match your robot’s action space, or leave compliance gaps when cameras capture bystanders, homes, or incidental personal data. This directory maps the major embodied AI data collection vendors to the capabilities that actually matter: sensor modality coverage, annotation depth, and compliance posture.

Vendors in this space fall into four practical categories: (a) end-to-end physical AI pipelines that handle capture through annotation, (b) annotation specialists that work from sensor logs you provide, (c) human-in-the-loop collection platforms, and (d) infrastructure or ecosystem connectors. Use those distinctions when scoping any RFP.

Vendor directory: who does what in embodied AI data collection

Scale AI (Physical AI)

Scale AI describes its Physical AI offering as an end-to-end data engine with a global collection network for robotics data. The positioning is clear: robotics training data must be created and sourced, not scraped. For embodied AI teams, that means Scale can support teleoperation-backed manipulation datasets and broader physical AI pipelines through its collection and annotation infrastructure.

Best for: End-to-end physical AI pipelines at enterprise scale. Compliance note: Request a Data Processing Agreement, sub-processor list, and data residency documentation before contracting. Verify during scoping: Exact sensor modality matrix (RGB-D vs. LiDAR vs. force/torque), synchronization specs, and coordinate frame conventions for your robot morphology.

Encord

Encord positions itself as an AI-native data infrastructure platform for physical AI and robotics. Its published framing covers collection, curation, annotation, and alignment of data across sensor streams, video, and text. The multimodal pipeline story is strong. Teams should confirm whether Encord is owning raw hardware capture, providing tooling for your capture, or doing both.

Best for: Multimodal sensor stream curation and annotation infrastructure. Verify during scoping: Capture vs. tooling split, synchronization granularity, and support for point clouds or IMU alignment.

iMerit

iMerit states it provides enterprise-grade robotics and physical AI data collection and annotation, including compliance-related language in its public materials. Its published focus on egocentric video and multimodal workflows makes it a reasonable candidate for teams building manipulation or egocentric demonstration datasets.

Best for: Enterprise robotics annotation with egocentric video support. Compliance note: iMerit includes compliance-forward language publicly; still request DPA documentation and anonymization QA process specifics. Verify during scoping: Modality-to-annotation-schema mapping and sample deliverables for your specific sensor stack.

Sama

Sama is a data annotation and labeling company offering human-in-the-loop annotation and validation for computer vision and multimodal workflows, including robotics and physical AI use cases. Its strength is annotation depth after capture rather than turnkey teleoperation hardware.

Best for: Complex annotation and validation of externally-captured robotics sensor logs. Verify during scoping: Whether Sama supplies end-to-end teleoperation rigs or primarily validates/annotates streams you provide.

Toloka

Toloka publishes robotics-specific materials describing collection and annotation workflows, including temporal and frame-level labeling for robotics training data. Its platform fits scalable human-in-the-loop pipelines well.

Best for: Scalable temporal annotation and robotics training data workflows. Verify during scoping: Which sensor modalities Toloka captures vs. which it annotates from provided logs; QA schema and acceptance criteria.

Appen and Shaip

Both Appen and Shaip serve robotics and physical AI data needs with human-in-the-loop collection and annotation capabilities. Shaip specifically addresses teleoperation and physical AI workflows in its positioning. Either can be evaluated for demonstration dataset collection and labeling tasks.

Best for: Human demonstration collection and annotation at scale. Verify during scoping: Sensor modality coverage, bystander consent handling, and whether teleoperation rig support is included.

Sensors and what “supported modalities” actually means in an RFP

Embodied AI projects draw on multiple sensor types. RGB cameras provide visual appearance and context. Depth cameras and RGB-D sensors add 3D geometry, critical for grasping and obstacle avoidance. LiDAR and point clouds support mapping and navigation at range. IMU data captures motion and kinematics (proprioception for locomotion and teleoperation). Force/torque and tactile sensors record contact events for manipulation tasks.

When a vendor says it supports a sensor modality, that phrase can mean raw stream delivery, processed outputs, or something in between. In any RFP, ask specifically: Do you accept raw sensor logs or processed outputs? What synchronization granularity is guaranteed (timestamps, frame rates)? What coordinate frame convention do you use, and how is calibration documented?

For bimanual manipulation, you’ll need synchronized RGB-D plus force/torque at minimum. Navigation datasets prioritize LiDAR/point cloud with IMU alignment. Locomotion and teleoperation learning projects typically require IMU, RGB, and pose streams time-stamped to a common clock.

Annotation depth: what to expect and what to demand

Basic annotation services deliver per-frame 2D bounding boxes and object classes. Embodied AI needs more. At minimum, expect temporal alignment across sensor streams, keypoints and pose estimates, 3D bounding boxes, and action segmentation that maps episodes to operator intent.

Deeper annotation includes affordance and grasp taxonomies, contact and contact-event labels, and instruction-to-episode linking when language conditioning is in scope. The ABC-130K teleoperation dataset referenced in open research illustrates the scale of bimanual manipulation coverage now expected from mature embodied datasets.

Ask vendors for a sample annotation schema before signing. Confirm that QA pipelines include inter-annotator agreement checks, physics-plausibility review for contact labels, and defined acceptance criteria with rejection rates.

GDPR and privacy: the compliance layer you can’t skip

Egocentric wearables and on-robot cameras don’t capture robots. They capture kitchens, offices, faces, and license plates. Even an industrial manipulation dataset collected in a warehouse may record workers or visitors. GDPR applies whenever personally identifiable information is present, regardless of whether capturing it was the intent.

A practical compliance checklist for any embodied AI data project:

• Documented consent workflows for all participants and bystanders where feasible
• Data minimization: mask or filter incidental faces, license plates, and background identifiers during annotation
• Data subject rights handling: access and deletion tracking if EU residents appear in the data
• Secure delivery with chain-of-custody documentation at each handoff
• Subprocessor transparency: full list of annotation vendors and infrastructure providers who touch the data
• Data residency documentation if cross-border transfers are involved

For vendors other than those with explicit public compliance statements, request a signed DPA, sub-processor list, retention and deletion schedule, and anonymization QA methodology before contracting.

Vendor selection checklist by project type

Teleoperation and manipulation

• Does the vendor capture synchronized RGB-D, force/torque, and pose streams, or only annotate them from your provided logs?
• What is the timestamp synchronization spec (clock source, max drift)?
• Are grasp, contact-event, and action segmentation labels in scope?
• Can the vendor produce demonstration datasets via teleoperation rigs, or does your team supply that hardware?

Navigation and mapping

• Does the vendor accept LiDAR point clouds and IMU logs as inputs?
• How is pose alignment handled across sensor modalities?
• Are failure-case labels (near-miss, obstacle collision, recovery) in scope?

Egocentric and human demonstration datasets

• What is the consent workflow for participants and bystanders?
• How are incidental faces and license plates handled (masking vs. blur vs. redaction)?
• What are the licensing boundaries on the collected footage?
• Is the dataset format compatible with retargeting to different robot morphologies?

For any vendor call, evaluate five dimensions: modality coverage match, annotation schema fit to your action space, willingness to provide sample data before contract, quality of compliance documentation, and delivery format compatibility with your training pipeline.

Where MatchPoint Studio fits in the pipeline

MatchPoint Studio works best as the capture and compliance layer that sits upstream of large-scale annotation vendors. Its role is planning and directing video capture for robotics and egocentric datasets, packaging metadata that makes sensor streams interpretable, and ensuring the collected footage arrives at annotation vendors with clean provenance and privacy controls already applied.

MatchPoint’s data collection pipeline includes participant consent workflows built for EU-standard projects, data subject rights management (access and deletion tracking), GDPR-compliant oversight embedded in project management, and secure delivery with continuous chain-of-custody documentation. For labeling, data minimization is applied during annotation: incidental faces and license plates are masked or filtered before datasets reach downstream annotation teams.

The practical model is straightforward. Use a robotics annotation specialist for large-scale labeling. Pair that with MatchPoint for capture direction, video quality control, and compliance-safe dataset packaging. The two functions don’t overlap, and the handoff is well-defined. That combination gives embodied AI teams annotation depth at scale without sacrificing the capture quality and compliance controls that protect the dataset’s long-term usability.

Every vendor in this directory has genuine strengths for specific project types. The selection decision comes down to which part of your pipeline needs the most support: end-to-end physical AI infrastructure, annotation depth on existing sensor logs, compliant egocentric capture, or the capture quality controls that make downstream annotation accurate in the first place.