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Here, we provide a single json file with a mapping from episode ID to three natural language annotations, for 75k success episodes in DROID (95% of all DROID success episodes).
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```
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import tensorflow as tf
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# DROID Annotations
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This repo contains additional annotation data for the DROID dataset which we completed after the initial dataset release.
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Concretely, it contains the following information:
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- the full language annotations for the DROID dataset (3x annotations for 95+% of the 75k successful demonstrations)
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- higher-accuracy camera calibrations (for 36k episodes)
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## Language Annotations
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The released RLDS dataset only contains a subset of the language labels.
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Here, we provide a single json file with a mapping from episode ID to three natural language annotations,
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for 75k success episodes in DROID (95% of all DROID success episodes).
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## Camera Calibrations
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The original DROID release included noisy camera calibrations. In post-hoc processing, we generated more accurate extrinsic camera calibration parameters
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for a subset of the DROID episodes. Concretely, we provide the following three calibration files:
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- `cam2base_extrinsics.json`: Contains ~36k entries with either the left or right camera calibrated with respect to base.
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- `cam2cam_extrinsics.json`: Contains ~90k entries with cam2cam relative poses and camera parameters for all of DROID.
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- `cam2base_extrinsic_superset.json`: Contains ~24k unique entries, total ~48k poses for both left and right camera calibrated with respect to the base.
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## Accessing Annotation Data
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All annotations are stored in `json` files which you can download from this repository.
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To access the respective annotation for a particular episode, you can compute the episode's ID.
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It corresponds to the file name `metadata_<episode_id>.json` in every episode's folder in the raw DROID data.
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To extract episode IDs and corresponding file paths, you can use the code below.
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You can then e.g. match the filepath to the one stored in the RLDS dataset if you want to match language annotations / camera extrinsics to RLDS episodes.
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```
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import tensorflow as tf
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