Optical Module Insertion 0731 TsFile
Apache TsFile edition of Xense/optical-module-insertion-0731, a
LeRobot v3.0 bimanual Flexiv Rizon 4 robotics dataset.
Source and attribution
- Original dataset: https://huggingface.co/datasets/Xense/optical-module-insertion-0731
- Publishing organization: XenseRobotics (
Xense) - Source authors: vertax42 and Xense Robotics Team
- Repository contributor:
xensedyl - License: Apache-2.0
- Task:
Pick up the optical module with the left hand, and insert it into the network port with the right hand. - Robot:
bi_flexiv_rizon4_rt - Train split: 111 episodes, 461,047 frame rows, one task, 30 fps, 54 Parquet files
- Source frame layout:
data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
The source card cites the LeRobot-Xense project:
@misc{vertax2026lerobotxense,
author = {vertax42 and Xense Robotics Team},
title = {LeRobot-Xense: LeRobot with Xense Tactile Robotics Support},
howpublished = {\url{https://github.com/Vertax42/lerobot-xense}},
year = {2026}
}
TsFile schema
Time = round(timestamp * 1000) as INT64 milliseconds and restarts at zero in
each episode. The source timestamp column is removed because it is exactly
represented by Time / 1000 seconds. index is renamed to sample_index, and
frame_index is retained.
| Columns | TsFile type | Role |
|---|---|---|
Time |
INT64/TIMESTAMP | TIME |
episode_index, task_index |
STRING device segments | TAG |
frame_index, sample_index |
INT64 | FIELD |
action_0 ... action_19 |
FLOAT | FIELD |
observation_state_0 ... observation_state_19 |
FLOAT | FIELD |
The 20 action and 20 state elements follow the source feature order. Each arm contains TCP x/y/z, six rotation representation values, r1 through r6, in the source-defined order, and a gripper position value. The left-arm values precede the right-arm values. Dots in source vector names are represented by the scalar field prefix and element index. No numeric rows, action dimensions, or state dimensions are omitted.
episode_index and task_index are stored through the TsFile table/device TAG
mechanism rather than duplicated as ordinary measurements.
Original videos
The 378 source MP4 files remain at videos/ and
are not included here. The path template is
videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4
for these streams:
observation.images.head(640 x 480, H.264, 30 fps)observation.images.left_wrist(640 x 480, H.264, 30 fps)observation.images.right_wrist(640 x 480, H.264, 30 fps)observation.images.left_tactile_0(700 x 400, H.264, 30 fps)observation.images.left_tactile_1(700 x 400, H.264, 30 fps)observation.images.right_tactile_0(700 x 400, H.264, 30 fps)observation.images.right_tactile_1(700 x 400, H.264, 30 fps)
Use episode_index and frame_index to align numeric rows with the matching
frame in each original per-episode video.
Read example
from tsfile import TsFileReader
reader = TsFileReader("data/xense_optical_module_insertion_0731.tsfile")
with reader.query_table(
"xense_optical_module_insertion_0731",
["episode_index", "task_index", "Time", "frame_index", "action_0"],
batch_size=4096,
) as result:
print(result.read_arrow_batch().to_pandas().head())
reader.close()
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