Robotics AI Data Operations

Ground-truth data operations for machines that move, grasp, and act in physical space

Robotics AI depends on a different class of data than most machine learning problems; one where a single mislabeled contact point, joint angle, or timestamp can be the difference between a model that works on a bench and one that fails on a factory floor.

Han Digital's Digital Operations practice runs the data pipelines behind robotics AI programs; structuring, labeling, and validating the multi-modal data that perception, manipulation, and motion-planning models are built on.

We work as an extension of a client's existing robotics stack inside their annotation tooling, their schema, their QA workflow; rather than routing data through a closed platform of our own.

Capability Areas

Perception & Scene Understanding

Object detection, instance and semantic segmentation, occlusion handling, and scene-graph labeling for robots operating in unstructured or semi-structured environments.

Manipulation & Grasp Data

Object pose estimation, grasp-point and contact-surface annotation, pre-grasp and post-grasp state labeling, and friction/material classification for pick-and-place and dexterous manipulation models.

Motion & Trajectory Labeling

6-degrees-of-freedom (6-DOF) trajectory annotation, waypoint definition, joint-angle labeling, and motion-constraint tagging for robotic arms and mobile manipulators.

Human-Robot Interaction (HRI) Data

Safety-zone labeling, collaborative workspace annotation, gesture and intent datasets, and proximity-event tagging for cobots and shared human-robot workspaces.

Facility & Fleet Operations Data

Bin-picking and clutter-scene annotation, conveyor and shelf-level object detection, deformable-object labeling, and path/lane annotation for warehouse and facility-scale robotic fleets.

Embodied & Foundation Model Data

Imitation-learning datasets, whole-body motion capture annotation, and environment-interaction labeling to support humanoid and general-purpose embodied AI training.

Multi-Sensor Fusion

Synchronized labeling across RGB-D, LiDAR, tactile, force-torque, and proprioceptive signal streams; aligned to a single time base for embodied AI and sensor-fusion models.