Datasets:
metadata
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- so100
- tutorial
- tsfile
- format:tsfile
- time-series
- timeseries
modality:
- tabular
- timeseries
configs:
- config_name: default
data_files:
- split: train
path: data/so100_brick.tsfile
size_categories:
- 10K<n<100K
so100_brick (TsFile)
Apache TsFile version of mldev19/so100_brick, a LeRobot v2.1 demonstration dataset recorded with an SO-100 arm.
Overview
This dataset was created using LeRobot. It records one SO-100 manipulation task (brick) with per-frame joint state and action vectors, plus two camera streams (laptop, phone).
- Robot: SO-100 (
so100) - Scale: 55 episodes, 16,279 frames, 1 task, 30 fps
- Split: train
Schema (TsFile structure)
The TsFile table is named so100_brick.
| Role | Columns |
|---|---|
| Time | Time, INT64 milliseconds |
| TAG | episode_index, task_index |
| FIELD | frame_index, sample_index |
| FIELD | action_0 … action_5, FLOAT |
| FIELD | observation_state_0 … observation_state_5, FLOAT |
The original 6-element action and observation.state vectors are flattened into scalar FLOAT measurements. The source index column is retained as sample_index.
Conversion notes
Time = round(timestamp * 1000)with millisecond precision; time restarts inside each episode, whileepisode_indexandtask_indexidentify the TsFile device.- The original
timestampfield is omitted because it is exactly represented byTime / 1000. - The two camera streams (
observation.images.laptop,observation.images.phone) are video and are NOT included in this repository; they remain in the source videos tree. meta/is mirrored from the source. Aside from the redundanttimestampcolumn and the excluded videos, no source rows or numeric fields are dropped.
Read example
from tsfile import TsFileReader
with TsFileReader("data/so100_brick.tsfile") as reader:
print(reader.get_all_table_schemas()["so100_brick"])
Source & license
- Original dataset: https://huggingface.co/datasets/mldev19/so100_brick
- Author: mldev19
- License: apache-2.0