README.md

metadata

license: cc-by-4.0
task_categories:
  - robotics
tags:
  - LeRobot
  - reassemble
  - contact-rich
  - manipulation
  - assembly
  - disassembly
  - franka
  - force-torque
size_categories:
  - 1M<n<10M

REASSEMBLE (LeRobot v3)

A LeRobot Dataset v3 port of REASSEMBLE — a multimodal dataset for contact-rich robotic assembly and disassembly on the NIST Assembly Task Board, recorded with a Franka arm.

This is a reformatted derivative, not the original release. The original data, full documentation, and canonical DOI are published by the authors at TU Wien: https://researchdata.tuwien.ac.at/records/0ewrv-8cb44 (DOI 10.48436/0ewrv-8cb44). Paper: arXiv:2502.05086 · Project: https://tuwien-asl.github.io/REASSEMBLE_page/ · Code: https://github.com/TUWIEN-ASL/REASSEMBLE

What this is

The original REASSEMBLE ships one HDF5 per recording session (encoded video blobs, raw PCM audio, a sparse event stream, multi-rate proprioception + force/torque, and hierarchical action-segment annotations). This port converts those 149 recordings into 149 LeRobot episodes (one recording = one long-horizon episode), with all sensors resampled onto a uniform 30 fps grid whose master clock is the hand-camera timestamps.

• Episodes: 149
• Frames: 1,433,406 @ 30 fps
• Robot: Franka
• Cameras: hand, hama1, hama2 (RGB 480×640) + event_cam (DAVIS render, 260×346)
• Per-frame task: the active high-level action segment's language label (e.g. "Insert USB.", "Pick square peg 3.")

Features

key	dtype	shape	notes
observation.images.hand / hama1 / hama2	video	480×640×3	RGB cameras
observation.images.event_cam	video	260×346×3	DAVIS event-camera render
observation.state	float32	(36,)	joint pos/vel/eff (7×3) + gripper (2) + EE pose (7) + EE velocity (6)
observation.state.joint_position	float32	(7,)
observation.state.gripper_position	float32	(2,)
observation.state.ee_pose	float32	(7,)	x,y,z + quaternion (w,x,y,z)
observation.force / observation.torque	float32	(3,)	measured F/T
observation.force.base / observation.torque.base	float32	(3,)	gravity/bias-compensated base F/T
action	float32	(9,)	next absolute EE target: pose (7) + gripper (2)
segment.success	bool	(1,)	whether the active segment succeeded
segment.index	int64	(1,)	high-level segment index within the recording

Fidelity notes (please read)

LeRobot is fixed-fps and frame-aligned, so this port makes deliberate tradeoffs:

• Resampling: every sensor is nearest-neighbour sampled to 30 fps. High-rate force/torque (~1.7 kHz measured, ~0.5 kHz base) is therefore downsampled in the frame stream.
• Audio is not a frame feature. It's preserved as a per-episode sidecar audio/episode_XXXXXX/{hand,hama1,hama2}.wav (16 kHz PCM). The LeRobot dataloader does not return audio tensors.
• Raw events are not a frame feature. The full sparse event stream is preserved losslessly as a per-episode sidecar events/episode_XXXXXX.npz (events: N×3 int64 x,y,polarity; timestamps: N float64). The event-camera render is available as the event_cam video.
• Missing cameras: a few recordings are missing a camera (e.g. 2025-01-10-16-17-40 has no hand cam — a known issue from the source README). Those frames are black-filled for the missing stream to keep the schema consistent.

Splits

Original author splits are preserved in meta/splits.json (per-episode recording + split): train = 111, test = 37, 1 unassigned.

Usage

from lerobot.datasets import LeRobotDataset

ds = LeRobotDataset("robot-lev/reassemble")
frame = ds[0]
print(frame["observation.state"].shape, frame["action"].shape, frame["task"])

Sidecars (audio / raw events) live alongside the dataset and can be downloaded with huggingface_hub:

from huggingface_hub import hf_hub_download
import numpy as np
ev = np.load(hf_hub_download("robot-lev/reassemble", "events/episode_000000.npz", repo_type="dataset"))
print(ev["events"].shape, ev["timestamps"].shape)  # (N, 3), (N,)

License & attribution

Released under CC-BY-4.0, inherited from the original dataset. You must credit the original authors:

Sliwowski, Daniel Jan; Jadav, Shail; Stanovcic, Sergej; Orbik, Jędrzej; Heidersberger, Johannes; Lee, Dongheui. REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly. TU Wien, 2025. DOI: 10.48436/0ewrv-8cb44.

@misc{sliwowski2025reassemble,
  title         = {REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly},
  author        = {Sliwowski, Daniel Jan and Jadav, Shail and Stanovcic, Sergej and Orbik, J\k{e}drzej and Heidersberger, Johannes and Lee, Dongheui},
  year          = {2025},
  eprint        = {2502.05086},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  doi           = {10.48436/0ewrv-8cb44},
  url           = {https://researchdata.tuwien.ac.at/records/0ewrv-8cb44}
}

Porting scripts & walkthrough: https://github.com/lvjonok/reassemble-lerobot-port — reproducible conversion pipeline, design tradeoffs, and operational gotchas.

This derivative reorganizes and resamples the data; refer to the original record for the authoritative, full-rate source.