Annotation

Robot data annotation for foundation-model training.

Convert raw robot, simulation, and egocentric episodes into subtask spans, action primitives, contact events, success/failure labels, reward signals, and QA metadata. Run by trained robotics annotators inside the SignIQ Platform.

What we annotate

Robot-aware label types, not generic bbox-and-mask.

Subtask spans

Per-frame interval labels mapping every episode to a hierarchy of named subtasks (reach → grasp → lift → place).

Action primitives

Atomic-action labels (open/close gripper, retract, pour, push) aligned to action chunks for ACT/Diffusion-style training.

Object interaction (HOI)

Per-episode target object lists, bounding boxes when needed, and hand-object contact events for VLA reasoning supervision.

Contact / force events

Onset timestamps for tactile array contacts and wrist F/T threshold crossings — synchronized to RGB and proprioception.

Success / failure

Outcome label per episode plus failure-mode tag with operator-rated severity (0→5 scale) for contrastive training and eval gating.

Reward labels

Per-step or sparse reward signals for offline RL, preference modeling, and reward-aware behavior cloning recipes.

Egocentric hand-object labels

Hand pose, finger articulation, gesture probabilities, and IMU streams aligned to first-person video for human-to-robot transfer.

Human-to-robot alignment

Subtask-span alignment between human egocentric clips and matched robot teleop episodes with alignment-confidence scores.

QA metadata

12-check schema validation, sync drift, joint-limit, NaN/Inf, calibration provenance, and per-episode trainability scores.

Workflow

Six steps, one platform.

01
Upload or collect

Episodes flow in from your captures or a SignIQ collection run.

02
Auto-segment

Motion-energy + action-magnitude segmentation proposes subtask boundaries.

03
Auto-label

VLM hindsight captions, action-primitive classifiers, and contact detectors run as a first pass.

04
Human review

Trained robotics annotators correct boundaries, fix mislabels, and verify outcome tags.

05
QA validate

Automated 12-check pipeline runs per episode; failures route back to review or are re-collected.

06
Export

Delivered LeRobot, GR00T-LeRobot, HDF5, or MCAP — schema-validated for your training stack.

Platform connection

Annotation projects run inside the SignIQ Platform.

The marketing site explains what we annotate. Project setup, operator assignment, review queues, QA dashboards, and exports happen inside the protected workspace. Pilots typically scope to one task family and 100–500 episodes.