Robotics data collection platform
An end-to-end platform for collecting, annotating, and delivering real-world training data for robot foundation models — from task execution through quality control to pipeline integration.
Definition
A robotics data collection platform is infrastructure purpose-built for producing the training data that Physical AI systems require. It coordinates human operators, sensor hardware, capture protocols, annotation pipelines, and data delivery into a single repeatable workflow.
Without a platform, robotics teams collect data ad hoc — stitching together consumer cameras, freelance annotators, and manual file transfers. The result is inconsistent quality, missing metadata, uncalibrated sensors, and datasets that cannot be reproduced or scaled. These problems compound as teams move from proof-of-concept to production, where data volume requirements increase by orders of magnitude.
A dedicated platform solves this by standardizing every step: how tasks are demonstrated, what sensors capture, how data is validated, and how it arrives in your training pipeline. The output is not just data but a reliable supply chain for it.
The problem
When different operators use different hardware at different times, the resulting data has variable resolution, frame rates, calibration, and coverage. Models trained on this data learn noise rather than signal. Debugging policy failures becomes impossible when you cannot trust the training data.
Ad-hoc collection often omits camera intrinsics, extrinsics, timestamps, or environment descriptions. Without this metadata, data cannot be used for 3D reconstruction, sensor fusion, or cross-session alignment. The data exists but is not usable for the models that need it.
Manual workflows break at scale. Coordinating 50 operators across 10 facilities with consistent protocols, synchronized sensor rigs, and centralized quality control requires infrastructure that spreadsheets and shared drives cannot provide. Data volume goes from days to weeks of collection time.
How it works
channels on one clock
rigs × cameras in the field, 50 more rigs deploying
1920×1200 global-shutter stereo RGB
IMUs at ~200 Hz
Each session records egocentric video from wearable rigs alongside third-person views, teleoperation joint data, and environment sensors. All streams are hardware-synchronized and timestamped to a common clock.
Every episode passes through structured annotation — action labels, object detections, temporal segmentation, success/failure flags — followed by multi-stage quality review before it enters your dataset. Rejected episodes are re-collected, not discarded.
Trained operators perform target tasks in real facilities using documented protocols. Each operator is certified on the specific task before recording begins.
Egocentric RGB-D, third-person video, teleoperation joint trajectories, hand-pose, force-torque, and IMU data are recorded simultaneously with calibrated, synchronized hardware.
Frame-level and episode-level labels are applied: action primitives, object bounding boxes, grasp types, success flags. Multi-reviewer QC ensures consistency across the dataset.
Validated data is packaged in standard formats (HDF5, RLDS, custom schemas) with full metadata, ready to ingest into your training infrastructure via API or bulk transfer.
Modalities
First-person RGB-D video from head-mounted, wrist-mounted, or chest-mounted cameras. Up to 1280x720 at 60 fps with synchronized depth. The primary modality for visuomotor policy learning.
Joint positions, velocities, and force-torque data recorded during human-in-the-loop teleoperation sessions. Action-labeled trajectories ready for behavior cloning and diffusion policy training.
Fixed and multi-angle camera views providing scene context, spatial relationships, and full-body operator motion. Used for scene understanding, multi-view reconstruction, and supervision signal augmentation.
21-joint hand skeleton data at 30+ fps, 6-DoF wrist and tool pose via IMU and visual-inertial odometry. Synchronized with all video streams for fine-grained manipulation modeling.
Temporal segmentation into action primitives (grasp, lift, transport, place, release), object bounding boxes with instance masks, success/failure flags, and task-level completion labels.
Camera intrinsics and extrinsics, facility floor plans, lighting conditions, object catalogs with dimensions and materials, and sensor calibration parameters for full reproducibility.
Industry applications
Assembly, bin picking, weld inspection, part insertion, quality control. Data collected on active production lines with real parts, real tolerances, and real cycle-time constraints. Includes object 6-DoF poses, gripper state, and contact force profiles.
Pick-and-place, palletizing, depalletizing, inventory scanning, package handling across thousands of SKU variations. Each episode labeled with grasp type, object category, weight range, and placement accuracy for training robust grasping policies.
Plating, bussing, drink preparation, room service, linen handling. Tasks involving deformable objects, liquids, and tight spaces where simulation cannot replicate the relevant physics. Data collected in commercial kitchens and hotel environments.
Platform vs. one-off
A one-off dataset is a snapshot: collected once, for one purpose, with one set of assumptions. When your model needs more diversity, different tasks, or new environments, you start from scratch. There is no continuity in hardware calibration, operator training, or annotation standards.
A platform provides continuity. The same calibrated hardware, the same trained operators, the same annotation protocols, and the same delivery pipeline produce data that is consistent across months and sites. You can request additional data for a new task or environment and receive it in the same format, with the same quality guarantees, ready to merge with your existing training set.
Integration
Data is only useful if it reaches your models. Humaid delivers validated datasets in the formats your training infrastructure expects — HDF5, RLDS, LeRobot, or custom schemas — with full documentation and loading utilities.
Each delivery includes camera calibration files, sensor specifications, episode metadata, and annotation schemas so your data engineering team can integrate without reverse-engineering the dataset structure. For teams using standard frameworks (PyTorch, JAX, TensorFlow), we provide dataloader examples and preprocessing scripts.
Ongoing collection contracts include versioned dataset releases, changelog documentation, and compatibility guarantees across batches so your training infrastructure does not break when new data arrives.
Data Explorer
The platform includes an integrated data explorer — a web-based interface where teams browse, inspect, and download collected datasets. The explorer surfaces synchronized multimodal recordings with full metadata, annotation overlays, and per-sequence file downloads. It connects the output of the collection pipeline directly to the teams that need to validate and use the data.
Instead of delivering opaque data archives, Humaid delivers browsable datasets. Clients can inspect individual sequences, verify annotation quality, and download exactly the files they need — egocentric video, hand pose data, object detection, action segmentation, or raw MCAP sensor streams. Open the data explorer (opens in a new tab).
FAQ
Get started
Tell us what your robot needs to learn. We will scope the collection, deploy operators, and deliver production-ready datasets integrated with your training infrastructure.