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Shotwell is an AI-powered robotics data annotation platform designed for analyzing robot training data. It processes raw robot videos, MCAP files, logs, images, and related artifacts, transforming long robot episodes into structured, searchable annotations for research, development, and quality assurance workflows. The platform segments videos into discrete subtasks, identifies failures within each step, and provides dense annotations with precise, sub-second timestamps. Shotwell also supports rubric-based pass/fail checks and SOP validation, helping teams evaluate robot performance, verify task execution, and improve the visibility of robotics datasets. By converting manual video review into structured episode data, Shotwell can support robot policy training, reinforcement learning workflows, failure analysis, and dataset observability. Results include subtask boundaries, task outcomes, attribute descriptions, and episode-level information that can be used to query and review robot behavior more efficiently.
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