Datasets:

XNX25
/

tmp_stacking_20260722_235152

license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - makermods
  - openbooth
  - MakerLab
  - tsfile
  - format:tsfile
  - time-series
modality:
  - timeseries
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/tmp_stacking_20260722_235152.tsfile

tmp_stacking_20260722_235152 (TsFile)

Apache TsFile version of makermods/tmp_stacking_20260722_235152.

Overview

This dataset is a LeRobot v3.0 demonstration of a stacking task recorded with an SO-100 follower arm.

Robot: SO-100 follower (so_follower)
Scale: 1 episodes, 575 frames, 30 fps
Split: train
Cameras (not uploaded): front and wrist

Schema (TsFile structure)

The TsFile table is named tmp_stacking_20260722_235152.

Role	Columns
Time	`Time`, INT64 milliseconds
TAG	`episode_index`, `task_index`
FIELD	`frame_index`, `sample_index`
FIELD	action_0 … action_5
FIELD	observation_state_0 … observation_state_5

Vector columns are flattened into scalar FLOAT measurements (single precision). The source index column is retained as sample_index.

Conversion notes

Time = round(timestamp * 1000) with millisecond precision; time restarts inside each episode, while episode_index and task_index identify the TsFile device.
The original timestamp field is omitted because it is exactly represented by Time / 1000.
Camera video streams are NOT included in this repository; they remain in the source videos tree.
meta/ is mirrored from the source. Aside from the redundant timestamp column and the excluded videos, no source rows or numeric fields are dropped.

Read example

from tsfile import TsFileReader

with TsFileReader("data/tmp_stacking_20260722_235152.tsfile") as reader:
    print(reader.get_all_table_schemas()["tmp_stacking_20260722_235152"])

Source & license

Original dataset: https://huggingface.co/datasets/makermods/tmp_stacking_20260722_235152
Author: makermods
License: apache-2.0