The EgoAtlas dataset family — real-world, high-fidelity, off the shelf or captured to order — and the expert services around it: scenario design, custom capture, annotation & QA, model evaluation, and failure mining & strategy.
Multiple scenes available and on demand. New scene families onboard in weeks, wherever your robots deploy.
Cooking, cleaning, laundry, tidying — long-horizon daily tasks in real homes.
Food prep, service, housekeeping and front-of-house operations.
Assembly, kitting, machine tending and intralogistics on live lines.
Regulated pharmacy workflows and laboratory automation, captured with compliance-trained staff.
Tote handling, picking, packing and ACR/AMR workflows on live floors.
Need a scene we haven't named? New scene families onboard in weeks — tell us where your robots deploy.
We design the scenario and capture the demonstrations — real environments, trained specialists, multiple scenes on demand, new scene families in weeks.
Auto pre-labeling plus expert human refinement, multi-tier review and statistical audits on every batch — dense annotation layers that arrive training-ready.
Real-task benchmarks and custom evaluations for your policies and VLA models — scores with evidence, batch-over-batch comparison, and the regression proof a release review can rely on.
Every miss attributed to its cause — data volume, scenario coverage, or annotation bias — then turned into strategy: what to collect, how much, in which scenes, and in what order. Grounded in your model's actual failure modes, not guesswork.
Loose boxes vs. training-grade labels — the difference a policy can feel.
We don't just record video. Vision, depth, force-torque, proprioception, end-effector trajectory, and language — hardware-timestamped to the same microsecond.
Ego and exo views of the full scene.
RealSense · 30 fps · 1080p
Metric geometry of objects and space.
stereo depth · LiDAR · registered
Contact, slip, and grasp quality.
6-axis F/T · 1 kHz · tactile skin
Joint angles, velocities, gripper state.
full kinematic state · per-frame
6-DoF pose and velocity of the hand.
position · orientation · velocity
Instructions plus causal rationale.
step labels · chain-of-thought
A pilot-scale engagement delivered in 2–4 weeks: training-ready dataset, validation report, baseline model evaluation. Or start with a single component — a quality-evaluation pass on your existing corpus.
Scale scene by scene, modality by modality — with quality gates on every batch and promises tracked to the day.
Evaluation and failure mining feed your next data round automatically — every cycle makes the model measurably better.
Complete episodes — video, synchronized sensor streams and annotation files together — so your team can evaluate structure and quality, not just watch clips.