README.md

metadata

license: mit
task_categories:
  - robotics
tags:
  - LeRobot
  - tsfile
  - format:tsfile
  - timeseries
size_categories:
  - 10K<n<100K

lerobot_pusht (TsFile format)

This dataset is a conversion of the Hugging Face dataset lerobot/pusht to Apache TsFile format. Original dataset: https://huggingface.co/datasets/lerobot/pusht

Dataset Description

Push-T is a robot-manipulation benchmark task introduced by Diffusion Policy (Chi et al. 2023): the agent must push a T-shaped block to a target pose. This dataset was collected with LeRobot (codebase v2.0) and is its low-dimensional state version — time series of 2-D end-effector state and 2-D action.

• Homepage: https://diffusion-policy.cs.columbia.edu/
• Paper: https://arxiv.org/abs/2303.04137v5
• License: MIT
• Size: 206 episodes / 25,650 frames / 1 task
• Sampling rate: 10 fps (100 ms per frame)

Original Data Structure

Column	Type	Description
observation.state	float32[2]	End-effector state (x, y)
action	float32[2]	Action (x, y)
episode_index	int64	Episode index
frame_index	int64	Frame index within the episode
timestamp	float32	Seconds elapsed within the episode
next.reward	float32	Reward
next.done / next.success	bool	Termination / success flags
index	int64	Global sample index
task_index	int64	Task index
observation.image	video[96×96×3]	(not included — this low-dimensional version has no images)

TsFile Conversion Notes

Conversion uses the "script preprocessing + Apache TsFile Java tool (schema mode)" path:

• Array expansion: observation.state[2] → state_0, state_1; action[2] → action_0, action_1 (kept as float32 → TsFile FLOAT).
• Column-name cleanup: . is replaced with _ (next.reward → next_reward, etc.).
• Time axis: Time = frame_index × 100 ms (10 fps), millisecond precision.
• Tag columns (device dimension): episode_id, task_id are declared as TsFile TAG, so each episode is an independent device with its own time axis starting at 0.
• ⚠️ Dropped columns: the original timestamp (per-episode elapsed seconds, float) is dropped because it repeats across episodes; an integer-millisecond time axis (frame_index × 100 ms) is used instead. All other columns are retained.

The converted table is named pusht, in a single file lerobot_pusht.tsfile with 25,650 rows.

Usage

# Read lerobot_pusht.tsfile with the Apache TsFile SDK
from tsfile import TsFileReader
reader = TsFileReader("lerobot_pusht.tsfile")
# table "pusht": tag columns episode_id / task_id, remaining columns are field measurements

Citation

@article{chi2024diffusionpolicy,
    author = {Cheng Chi and Zhenjia Xu and Siyuan Feng and Eric Cousineau and Yilun Du and Benjamin Burchfiel and Russ Tedrake and Shuran Song},
    title ={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
    journal = {The International Journal of Robotics Research},
    year = {2024},
}