Datasets:
File size: 2,830 Bytes
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license: apache-2.0
task_categories:
- robotics
tags:
- tsfile
- timeseries
- time-series
- LeRobot
- robotics
- format:tsfile
pretty_name: grab_dibble
size_categories:
- 1K<n<10K
---
# grab_dibble (TsFile)
Apache TsFile version of [`ymatari/grab-dibble`](https://huggingface.co/datasets/ymatari/grab-dibble).
## Overview
A LeRobot v2.1 single-arm manipulation dataset recorded on a SO-101 follower
robot. The arm grasps a dibble and places it into a container. Each frame holds
the 6-DOF joint positions (shoulder pan/lift, elbow flex, wrist flex/roll,
gripper) for both the commanded `action` and the observed `observation.state`,
plus three camera views (top, surface, wrist) that are stored as videos in the
original dataset.
- **Episodes:** 50
- **Frames:** 7,642
- **Sampling rate:** 30 fps
- **Tasks:** 1 — "place the dibble in the container"
## Schema (TsFile structure)
All episodes share one TsFile with `episode_index` and `task_index` as TAG
columns; query a single episode with `WHERE episode_index = N`.
- **Time** (INT64, milliseconds) — `round(timestamp * 1000)`; the source
`timestamp` column is dropped (it equals Time / 1000).
- **episode_index** (TAG) — episode identifier.
- **task_index** (TAG) — task identifier (single task here).
- **frame_index** (INT64) — per-episode frame counter.
- **sample_index** (INT64) — source `index` column, renamed.
- **action_0..action_5** (FLOAT) — commanded joint positions.
- **observation_state_0..observation_state_5** (FLOAT) — observed joint positions.
The 6 action/state dimensions map to `shoulder_pan.pos`, `shoulder_lift.pos`,
`elbow_flex.pos`, `wrist_flex.pos`, `wrist_roll.pos`, `gripper.pos`.
## Usage
Install the Apache TsFile Python SDK (`pip install tsfile`) and read a converted file:
```python
from pathlib import Path
from tsfile import TsFileReader
path = Path("data/grab_dibble.tsfile")
with TsFileReader(str(path)) as reader:
schemas = reader.get_all_table_schemas()
print("tables:", list(schemas))
table_name = next(iter(schemas))
table = schemas[table_name]
columns = [column.get_column_name() for column in table.get_columns()]
print("columns:", columns)
field_names = [
column.get_column_name()
for column in table.get_columns()
if column.get_column_name() not in {"Time", "time"}
]
if field_names:
with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
batch = result.read_arrow_batch()
if batch is not None:
print(batch.to_pandas().head())
```
## Source & license
- Original dataset: https://huggingface.co/datasets/ymatari/grab-dibble
- Author / publisher: ymatari
- License: apache-2.0
- Note: camera videos (top/surface/wrist) are NOT included; see the original dataset.
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