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How do you determine whether teleoperation or human video data is of sufficient quality for training? A discussion on data quality becoming a bottleneck

Pantheon's dataset audit released last month found issues in several popular open-source robotics datasets, including frozen or disconnected wrist cameras, gripper actions recorded about two frames earlier than the actual motion, recording stopping mid-action, and long idle tails after task completion; the RoboDrop paper (arXiv 2609.10021) also points out that execution errors, sensor drift, and timestamp misalignment are common errors.

Part of the event: Discussion: Teleoperation and Human Video Data Quality as a Bottleneck in Robot Training

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