Datasets:

ICoM-RWTH
/

DIEPF_2026

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              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 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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.

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DIEPF 2026 – Depth–Image + Extrusion + (Robot) Pose Fusion Dataset for Large-Scale Additive Manufacturing

This repository contains multi-modal recordings for construction-scale additive manufacturing experiments, combining RGB-D imagery with robot kinematics and process signals. The dataset is designed for quality monitoring, defect detection, and dataset generation for learning-based approaches in robotic extrusion processes.

Hardware & setup

Robot: KUKA KR210 R3100 with KRC4 controller
Camera: Intel RealSense D405 (short-range RGB-D), eye-in-hand on robot flange, viewing the nozzle at a fixed angle
Robot telemetry: streamed via KUKA variables (OpenShowVar / VAR proxy), including joint axes and additional process variables (e.g., extruder RPM and override)

What’s inside

The dataset is organized as one .tar archive per recording session.

Recording archives (download sizes)

The dataset is provided as one TAR archive per recording session (stored via Git LFS).

Total size (all sessions): ~7.9 GB

Session	Archive	Size
001	`260109_Recording_001.tar`	82.2 MB
002	`260109_Recording_002.tar`	84.1 MB
003	`260109_Recording_003.tar`	1.59 GB
004	`260109_Recording_004.tar`	656 MB
005	`260109_Recording_005.tar`	596 MB
006	`260109_Recording_006.tar`	467 MB
007	`260109_Recording_007.tar`	700 MB
008	`260109_Recording_008.tar`	2.05 GB
009	`260109_Recording_009.tar`	2.23 GB

Archive size scales with recording duration and camera FPS. Larger sessions typically contain longer continuous runs.

Data format notes

Image rotation

The RGB-D camera is mounted eye-in-hand; images may be rotated (e.g., 180°) before saving to match a consistent orientation. Applied rotation should be documented in per-session metadata.

Example: download and extract one session (Python)

from huggingface_hub import hf_hub_download
import tarfile
from pathlib import Path

repo_id = "ICoM-RWTH/DIEPF_2026"
filename = "recordings/260109_Recording_001.tar"

tar_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=filename)
out_dir = Path("extracted/260109_Recording_001")
out_dir.mkdir(parents=True, exist_ok=True)

with tarfile.open(tar_path, "r") as tar:
    tar.extractall(out_dir)

print("Extracted to:", out_dir)

Add this (citation)

## Citation

```bibtex
@dataset{diepf2026,
  title     = {DIEPF 2026: Depth--Image + Extrusion + Robot Pose Fusion Dataset for Large-Scale Additive Manufacturing},
  author    = {Benz, Hendrik and Nguyen Trong, The Vinh},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/ICoM-RWTH/DIEPF_2026}
}

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