The 6 Best Data Collection Services for Robotics and Embodied AI [2026]
Robotic Learning Lead at Encord
TL;DR: Data collection for robotics and embodied AI means capturing teleoperation demonstrations, egocentric footage, and synchronized multi-sensor data that physical AI models learn from. It's harder than standard computer vision data collection because every frame has to line up across multiple sensors and pair with the exact action the robot took. Encord is the most complete platform for it, built to handle teleoperation, multimodal sensor data, and annotation in one place. Other vendors specialize in different parts of the problem: Scale AI and Appen bring scale, Kognic owns sensor fusion for autonomous driving, MatchPoint Studio and iMerit offer more custom or domain-specific programs. If you're not ready to commission custom data yet, public datasets like Open X-Embodiment and Encord's own public robotics dataset are a reasonable place to start.
Why data collection is the bottleneck in robotics right now
Robots are moving out of research labs and into warehouses, kitchens, and hospitals faster than most teams expected. Humanoids, robotic arms, autonomous mobility platforms all depends on one thing that's easy to underestimate: good training data.
Here's what makes collecting good training data harder than it sounds. A language model can learn from text already sitting on the internet. A robot can't. There's no public archive of "what it feels like to pick up a mug" the way there's a public archive of "what a cat looks like." Every robot has to learn from data someone physically collected: a person operating a robot arm, a camera mounted on someone's chest, a LiDAR sensor scanning a warehouse floor. And all of that has to be captured in sync, down to fractions of a second, or the model learns the wrong lesson.
This guide breaks down what robotics data collection actually involves, the types of data it covers, the vendors worth knowing in 2026, and what public datasets can help you get started before you invest in custom collection.
What is data collection for robotics and embodied AI?
Data collection for robotics and embodied AI is the process of capturing the sensor data and actions a robot needs to learn how to see, move, and interact with the physical world. Unlike text or image data, this data has to be physically gathered through teleoperation, wearable cameras, or sensor rigs, and it only becomes useful once everything lines up in time and space.
What types of data need to be collected?
| Data type | What it captures | Common use case |
| Teleoperation data | A human operating a robot arm or rig, recorded action by action | Imitation learning for manipulation tasks |
| Egocentric data | First-person footage from a chest-mounted or robot-mounted camera | Matching the robot's own viewpoint during deployment |
| Multimodal sensor data | LiDAR, depth, proprioception, and force/torque, captured together | Sensor fusion for perception and control |
| Simulation-augmented data | Synthetic data generated in a simulated environment, then validated against reality | Scaling up training data cheaply before real-world capture |
| Video and image sequences | Standard camera footage of scenes and objects | Scene understanding and object interaction |
Where this data actually gets used
- Manipulation and dexterity: grasping, tool use, and fine motor tasks like folding or assembly
- Mobility and navigation: humanoid and quadruped locomotion, warehouse and outdoor navigation
- Human-robot interaction: collaborative tasks where a robot and a person work side by side
Key Factors to consider when choosing the right Data Collection vendor
Not all data collection vendors solve the same problem, so the right choice depends on the current stage of production and the specific data your model actually needs.
Here's what to look at before you commit to one:
- Modality coverage: Ensure the vendor can handle teleoperation, multimodal sensor fusion, and egocentric data capture together, or you might need to bring in separate tools for each. A vendor that only does one modality well often means more integration tools later.
- Hardware and rig compatibility: Ensure that the vendor already supports your specific robot platform, or do they need to build compatibility from scratch? This affects both cost and how quickly you can start collecting usable data.
- Automation and QA: Sensor sync issues and mislabeled action-pair data are easy to miss manually and expensive to catch later. Look for vendors with automated checks built in, not just a human review pass at the end.
- Data security: Robotics data is often proprietary and tied to unreleased hardware or products. Check where the data is stored, who has access to it, and whether the vendor meets the compliance standards your team or customers require (SOC 2, GDPR, and similar)
- Scalability: A vendor that works well for a 50-episode pilot data might not hold up at production scale, whether that's cost, throughput, or quality consistency. It's worth asking upfront how the vendor's process changes as volume grows, rather than finding out after you've scaled.
| If you need... | Start with |
| Data collection and labeling for robotics and physical AI, across teleoperation, egocentric, and multi-sensor data | Encord |
| A large-scale autonomous vehicle data program only | Scale AI |
| A large annotator workforce for 2D and language-paired data | Appen |
| Sensor fusion specifically for AV/ADAS driving scenes | Kognic |
| A custom, compliance-focused capture program | MatchPoint Studio |
| Collection bundled with domain-specific QA | iMerit |
The 6 best data collection vendors for robotics and embodied AI
1. Encord (End-to-end data collection and labeling for robotics and physical AI)
Key features:
- Collects and labels teleoperation data, egocentric video, multimodal sensor data (LiDAR, depth, RGB, proprioception, force/torque), and UMI gripper data, all within one platform
- In-field operators and reconfigurable lab facilities for custom capture, alongside a public dataset library to get started faster
- Automated sync validation and QA across sensor streams, so multimodal data stays usable instead of falling out of alignment
- Model-assisted labeling and active learning built in, so collection and annotation feed into each other rather than sitting in separate tools
- SOC 2 and GDPR compliant, with data staying in your own cloud
- Supports household, industrial, and commercial environments, and both manipulation and mobility use cases
Best for: Robotics and physical AI teams who need data collection and labeling to work together in one platform, across any combination of teleoperation, egocentric, and multi-sensor data.
2. Scale AI
Key features:
- Established data programs specifically for autonomous vehicle perception and planning
- Human-in-the-loop QA built for high-volume AV data
- Strong support for dense video and driving trajectory capture
Best for: Teams running large-scale autonomous vehicle programs, rather than general robotics or manipulation-focused data collection.
3. Appen
Key features:
- Native 3D point cloud editing with calibrated camera, LiDAR, and radar fusion, purpose-built for driving scenes
- Over 100 million annotations delivered for automotive OEMs and Tier 1 suppliers
- 90+ automated quality checks tuned specifically for AV/ADAS sensor data, with TISAX Level 3 and ISO 27001 certification
Best for: Teams building ADAS or autonomous driving systems with heavy camera/LiDAR/radar fusion needs. Doesn't cover general robotics data types like teleoperation or egocentric capture, or non-driving computer vision use cases.
4. Kognic
Key features:
- Native 3D point cloud editing with calibrated camera, LiDAR, and radar fusion, plus temporal sequence handling
- Over 100 million annotations delivered across production programs for major OEMs and Tier 1 suppliers
- More than 90 automated quality checks built specifically for sensor data, with TISAX Level 3 and ISO 27001 certification
Best for: Teams running ADAS, autonomous driving, or robotics programs with heavy multi-sensor data and European OEM relationships. Worth noting: it doesn't cover audio data or general-purpose computer vision like medical imaging.
5. MatchPoint Studio
Key features:
- Custom RGB and RGB-D capture built around controlled manipulation conditions
- GDPR-compliant data delivery with clear project documentation
- Direct capture capability, not just annotation on top of someone else's data
Best for: Teams that need a bespoke capture program with clear compliance paperwork, at a smaller scale than the larger enterprise vendors.
6. iMerit
Key features:
- Domain-trained QA reviewers with healthcare, agriculture, and retail experience
- End-to-end support from data collection through model validation
- Real experience supporting robotics deployments alongside broader computer vision work
Best for: Teams that want collection support paired with strong domain-specific quality review, rather than a dedicated robotics-only capture specialist.
Public robotics datasets worth knowing
Custom data collection isn't always the first step. Several open, publicly available datasets, including Encord's own data catalogue can help you get a model off the ground before you invest in bespoke capture:
| Dataset | What it offers | Best for |
| Encord's 1k robotics data sample library | Free, browsable catalog of teleoperation, RGB-D egocentric (VLA-captioned), and UMI gripper data across household, industrial, and commercial environments | Getting started before committing to custom collection |
| Open X-Embodiment (OXE) | Over 1 million real-robot trajectories across 20+ robot types | Cross-embodiment pretraining |
| DROID | Around 92,000 manipulation trajectories across 564 real-world scenes | Testing how well a model generalizes across scenes |
| BridgeData V2 | A diverse, multi-task manipulation dataset | General-purpose transfer learning |
The tradeoff is straightforward: public datasets are free and immediate, but they won't match your specific robot, environment, or task. Once you know exactly what your model is missing, that's when custom collection from vendors like Encord starts to matter.
Why Encord leads in robotics and embodied AI data collection
- Handles teleoperation, egocentric capture, and multimodal sensor fusion natively, in one workflow
- Automation and QA built specifically for the complexity of physical AI data, not adapted from a general-purpose tool
- Covers the full lifecycle collection, curation, annotation, and active learning, without handing data off between separate vendors
- Enterprise-grade compliance for sensitive, proprietary robotics data
- Offers a public dataset as a low-risk way to get started before scaling into custom collection
Key takeaways
- Robotics and embodied AI data collection covers teleoperation, egocentric footage, and tightly synchronized multi-sensor data, a harder problem than standard image or video collection
- Vendors specialize differently: Kognic focuses on sensor fusion for autonomous driving, Appen and iMerit bring workforce scale and domain QA, MatchPoint Studio offers custom compliance-focused programs, and Scale AI brings established high-volume capacity
- Public datasets like Open X-Embodiment, DROID, and AgiBot World are a legitimate starting point, but they can't replace data collected for your specific robot and task
- Encord is the most complete option for teams that want collection, annotation, and QA in one platform, and it offers a public dataset as an easy on-ramp
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Frequently asked questions
Because robots can't learn from data scraped off the internet the way language models do. Every useful robotics dataset has to be physically collected, with sensor data and the robot's actions captured together and lined up in time.
Encord handles teleoperation, egocentric capture, and multimodal sensor fusion in one workflow, with automated QA and compliance built in — so teams don't need to stitch together separate tools for collection and annotation.
Through in-field operators and lab facilities running leader/follower teleoperation rigs, with data flowing directly into Encord's platform for sync validation, curation, and annotation.
Public datasets are a good starting point for early development and benchmarking, but they're unlikely to match your exact robot, environment, and tasks. Most production systems end up combining public data with custom collection to close that gap.