We Looked at the Data
It's well-known that robotics data is messy. For the data to be suitable for training, several unstable input streams must be well-formed and perfectly in sync. Many different data corruption errors, ranging from very obvious to very subtle, can silently damage training runs and raise the noise floor. Without clean underlying data, strong model architectures can fail to outperform weaker, but more
It's well-known that robotics data is messy. For the data to be suitable for training, several unstable input streams must be well-formed and perfectly in sync. Many different data corruption errors, ranging from very obvious to very subtle, can silently damage training runs and raise the noise floor. Without clean underlying data, strong model architectures can fail to outperform weaker, but more noise-resistant ones. We looked at several major open-source UMI and teleoperated bimanual 6-DOF arm + 1-DOF gripper datasets to diagnose and correct issues. We're releasing per-episode manifests…
related reading
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