Annotation built
for robotics.
Dense, frame-accurate action labels for robot manipulation data — delivered in hours, at a fraction of manual labeling cost.
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From raw episodes to training-ready data.
A single pipeline that turns messy robot footage into dense, quality-scored action labels.
Ingest your episodes
Connect raw teleop video, robot logs, and multimodal sensor streams. Shotwell ingests episodes at any scale — no reformatting required.
Dense, frame-level labeling
Our models watch every frame, segment continuous motion into discrete actions, and label each one against your task definition and SOP rubric.
QA'd, training-ready export
Every label is scored for quality and returned in hours — not weeks — ready to drop straight into your training and evaluation pipeline.
Built for the hardest robotics data.
Wherever motion is continuous and quality is non-negotiable, Shotwell produces labels your models can trust.
Manipulation & grasping
Frame-accurate labels for pick, place, and regrasp sequences across single- and bimanual arms.
Deformable & folding tasks
Dense action segmentation for cloth, cables, and other deformables where every frame matters.
Teleoperation episode QA
Automatically flag failed, noisy, or off-task episodes before they ever reach your training set.
Vision-language-action data
Task-aligned action labels and language grounding to fuel VLA and foundation-model post-training.
Multi-camera & multimodal fusion
Consistent labels across synchronized camera views, depth, and proprioceptive sensor streams.
Long-horizon segmentation
Break multi-minute episodes into clean, discrete sub-tasks scored against your SOP rubric.
Segment your training videos into labeled actions.
Shotwell watches every video frame-by-frame, chunks it into discrete actions, and labels those actions on quality and task definition.
Quality is the biggest lever.
Data quality moves robot model performance more than anything else. Shotwell is built to maximize it at every frame.
