Embodied AI Training Data: Demonstrations for Robot Policies & VLAs
Embodied AI training data for robot policies and vision-language-action models: real human demonstrations with synced depth, tactile, dual-wrist views and 21-point hand pose, action-labelled and LeRobot-ready. This is embodied AI in the robotics sense, not embodied cognition.
What's in Our Embodied AI Dataset
Every episode is one synchronized bundle for training robot policies and VLAs: multi-view video, metric depth, tactile, IMU and per-frame action labels.
Action-Labelled Demos
Per-frame action labels retargetable to a robot end-effector.
Depth and Tactile
Metric stereo depth and 16x16 tactile force, beyond mono RGB.
Dual Wrist Views
Left and right wrist cameras, the view a robot has at execution.
Hand to Gripper Mapping
21-point hand pose that retargets to grippers and dexterous hands.
Task Diversity
Real factory, workshop and service tasks, not tabletop toys.
Failure and Correction
Real mistakes and recoveries, the data teleop sets rarely contain.
Why Human Demonstrations, and Where They Fit
Human demonstrations scale far cheaper than teleop and reach environments a robot can't, but they carry an embodiment gap: a human hand is not a parallel gripper. We make that gap tractable with metric depth and 21-point hand pose, so reach, grasp and place retarget cleanly. In-hand reorientation is the honest hard case.
We make the embodiment gap tractable.
Depth, tactile, dual wrist and 200Hz IMU on one clock: the multimodal signal a policy needs, and the one mono video can't provide. Metric depth and 21-point hand pose retarget human reach, grasp and place cleanly to an end-effector.
Where Our Data Gets Used
Every episode ships LeRobot-ready, with RLDS/TFDS and ROS 2 / Foxglove MCAP on request, so it loads straight into an OpenVLA, pi0 or GR00T fine-tune.
Fine-tuning a VLA
OpenVLA, pi0 or GR00T on real, language-conditioned demonstrations.
Imitation learning
Human video with action labels for behaviour cloning and policy learning.
Contact-rich manipulation
Grasp, slip and insertion, backed by the tactile and depth modalities mono video never sees.
Custom task taxonomy
Your eval's exact tasks and environments, captured to spec at partner sites rather than a generic corpus.
Managed capture where the tasks live
We run consent-first capture at partner sites — factories, workshops, repair bays and craft studios that gig marketplaces and lab teleop cannot reach — with provenance you can audit.
Delivery into your training stack
Datasets ship LeRobot-ready and load with LeRobotDataset, with RLDS/TFDS and ROS 2 / Foxglove MCAP on request, so there is no conversion project before you can train.
Retargeting human motion to your embodiment
Metric depth and 21-point hand pose retarget human reach, grasp and place cleanly to a parallel gripper or dexterous hand — the retargeting step raw video alone can't support.
Synced multimodal ground truth
Depth, tactile, dual wrist and 200Hz IMU on one clock — the multimodal signal a policy needs, and the one mono video cannot provide.
Fine-tuning a Policy or VLA?
Tell us the tasks and embodiment. We'll map the capture and send a representative sample pack — the same modalities, calibration and delivery format you'd get in production, so your team can inspect sensor alignment before any commitment.
Book a Strategy Session →When You Need Capture to Your Taxonomy, Not a Generic Corpus
Off-the-shelf robot corpora cover common pick-and-place well. But teams pushing real embodied AI need capture a scraped or teleop-only dataset structurally can't deliver:
Send your task taxonomy and we scope a capture program against it — sites, objects, action set, episode counts and acceptance criteria — so the data you train on is your data, captured where the tasks actually live, not a generic corpus everyone else already has.
Frequently Asked Questions
Every episode is captured under a signed release permitting commercial training, with an auditable per-episode chain of custody. Non-exclusive by default; exclusive and custom terms available.
Yes. Datasets ship LeRobot-ready and load with LeRobotDataset, and are available as RLDS/TFDS for OpenVLA-style pipelines or ROS 2 / MCAP on request.
Through retargeting. Metric depth and 21-point hand pose map human motion to an end-effector for reach, grasp and place. Budget for the retargeting step, which is why we ship the depth and pose that make it work.
Start with the sample pack, a representative delivery with all modalities and calibration, so your team can inspect sensor alignment first.
No. Embodied AI here means AI that perceives and acts in the physical world, robot policies and VLAs. Embodied cognition is a separate psychology and philosophy theory.
This is human demonstration data, so it complements a robot-trajectory corpus like Open X-Embodiment rather than replacing it. Most teams train on both and budget for the retargeting step between our hand pose and a robot action space; note that sim benchmarks under-measure the contact modalities.
Related Solutions in the Physical-AI Cluster
Physical AI Training Data — Multimodal Field Capture for Robot Learning
Learn more →World Model Training Data — Real Video for World Foundation Models
Learn more →Tactile Manipulation Data — The Modality Simulation Can't Fake
Learn more →Robot Data Collection Services — Teleoperation & Egocentric Capture
Learn more →Physical AI & Robotics — Consulting, Development & Training Data
Learn more →Private & On-Premise AI — Self-Hosted AI Deployment
Learn more →AI Transformation Workshop
Half-day strategy workshop to map your manipulation tasks and target embodiment, and identify the right capture program for your policy or VLA stack.
Book a workshop →AI Strategy Session
30-minute scoping call. We'll talk through your tasks, target model — OpenVLA, pi0 or GR00T — and delivery format, then scope a representative sample pack.
Book a session →AI Consultant vs In-House Team
Honest tradeoffs on standing up embodied-AI demonstration capture in-house versus engaging a partner for a scoped, delivered capture program.
Read the comparison →See the Data Before You Decide
A 30-minute strategy call. We'll walk through your tasks and target embodiment, then scope a representative sample pack so your team can inspect sensor alignment and evaluate it in an afternoon.