Datasetpublic

djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128

Robometer Annotations Dataset This dataset contains one Robometer annotation bundle per episode. Files episodes/episode_000000.npz: Per-episode arrays aligned to the original frame indices episodes.parquet: One manifest row per episode, including episode_index episodes.jsonl: The same manifest in JSONL format run_metadata.json: Run-level metadata and export settings Usage import numpy as np import pandas as pd from huggingface_hub import… See the full description on the dataset page: https://huggingface.co/datasets/djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128.

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Dataset Card

Robometer Annotations Dataset

This dataset contains one Robometer annotation bundle per episode.

Files

—episodes/episode_000000.npz: Per-episode arrays aligned to the original frame indices
—episodes.parquet: One manifest row per episode, including episode_index
—episodes.jsonl: The same manifest in JSONL format
—run_metadata.json: Run-level metadata and export settings

Usage

python

import numpy as np
import pandas as pd
from huggingface_hub import hf_hub_download

# Load the episode manifest
manifest_path = hf_hub_download(repo_id="djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128", filename="episodes.parquet", repo_type="dataset")
manifest = pd.read_parquet(manifest_path)

# Download one episode bundle
row = manifest.iloc[0]
episode_path = hf_hub_download(repo_id="djkesu/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128", filename=row["episode_npz_path"], repo_type="dataset")
episode = np.load(episode_path)

episode_index = int(row["episode_index"])
dataset_indices = episode["dataset_indices"]
frame_success_probs = episode["frame_success_probs"]

Generation Parameters

{
  "args": [
    "--upload-only",
    "--output",
    "/outputs/trlc_tshirt_folding_robometer_laundromat_fold_20260314_fixed128"
  ]
}