Bounding box annotation helps robots detect and track objects by drawing precise rectangular boxes around them. This technique enhances AI models for autonomous navigation, object manipulation, and real-time decision-making in robotics applications.
Skeleton annotation maps key points on objects or human bodies, enabling robots to analyze posture, gestures, and movement. This enhances AI-driven applications in human-robot interaction, motion tracking, and assistive robotics.
Unitlab supports synchronized camera views, image, video, audio, text, documents, geospatial imagery, and connected sensor records.
Yes. Related views can be grouped with shared temporal context so objects, actions, and properties stay consistent across cameras.
Teams can use boxes, segmentation, polygons, keypoints, object tracks, temporal labels, classifications, properties, and relations.
Yes. Frame-accurate timelines, keyframes, tracking, temporal ranges, and synchronized playback support long-form demonstrations and episodes.
Ontologies, properties, relations, event labels, and sequence context can encode interactions without flattening multimodal data.
Teams can search, filter, deduplicate, balance, version, and route selected robot data into annotation and QA.
Yes. Bring Your Own Model workflows support domain-specific pre-labeling and model-in-the-loop correction.
Instructions, reviewer roles, issues, rework, approvals, and full history help standardize labels across teams and tasks.
Unitlab supports large datasets, long sequences, dataset versions, collaborative workflows, and programmatic upload and export.
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