Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Paper • 2303.04137 • Published • 6
Datasets:
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "tsfile/tsfile_py_cpp.pyx", line 567, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
tsfile.exceptions.FileOpenError: 28:
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
scan = self._scan_metadata(all_files)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
with self._open_reader(file) as reader:
~~~~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
return TsFileReader(file)
File "tsfile/tsfile_reader.pyx", line 323, in tsfile.tsfile_reader.TsFileReaderPy.__init__
SystemError: <class '_weakrefset.WeakSet'> returned a result with an exception set
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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 → TsFileFLOAT). - 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_idare 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},
}
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