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license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- isaac-sim
- assembly
- manipulation
- imitation-learning
- contact-rich
configs:
- config_name: default
data_files: data/*/*.parquet
---
# AssembleBench
Multi-task contact-rich assembly demonstrations for imitation learning / VLA fine-tuning,
and the training set behind the [AssembleBench](https://github.com/hud-evals/assemble-bench)
benchmark: 14 tasks on the NIST ATB-1 taskboard (insert round and square pegs, mesh gears,
thread nuts) on the DROID robot platform.
Read the writeup: [Benchmarking Robot Models on Contact-Rich Assembly](https://www.hud.ai/blog/assemble-benchmark).
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
<a class="flex" href="https://huggingface.co/spaces/lerobot/visualize_dataset?path=hud-evals/AssembleBench">
<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/>
<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-dark-xl.svg"/>
</a>
## Summary
| | |
|---|---|
| Episodes | **1355** |
| Frames | **444651** |
| FPS | 15 |
| Tasks | 14 (pegs, gears, nuts; **no M20**) |
| Action | DROID 8-D joint position (7 arm + binary gripper) |
| Cameras | `observation.images.front`, `observation.images.wrist` at 180x320 |
| Proprio | `observation.state` (8), `joint_vel` (7), `eef_pos` (3), `eef_quat` (4) |
## Tasks (episode counts)
Language-conditioned pick / insert / mesh / thread:
| family | task | n |
|--------|------|---|
| peg_round | 4 / 8 / 12 / 16 mm | 96 / 99 / 100 / 100 |
| peg_square | 4 / 8 / 12 / 16 mm | 98 / 98 / 99 / 100 |
| gear_mesh | small / medium / large | 92 / 97 / 92 |
| nut_thread | M8 / M12 / M16 | 90 / 95 / 99 |
**M20 removed:** embedding analysis (PCA / t-SNE) showed M20 as a separate mode that split the nut cluster; it is not included.
## How it was built
Built from a cleaned and topped-up revision of
[`lukasskellijs/assembly_bench_2`](https://huggingface.co/datasets/lukasskellijs/assembly_bench_2),
with a second QC pass for duplicates, idle chunks, length outliers, and within-family
Mahalanobis outliers. Pipeline:
1. Start from the cleaned v2 revision (911 eps: dups + long outliers already removed from the original 1500).
2. Drop all **M20** nut episodes.
3. Append scripted-expert top-up HDF5 demos that refill the gaps left by the clean pass (success-filtered Isaac Lab recordings).
4. QC blocklist (45 trajectories by MD5 of full `action || state`):
- exact-duplicate extras (none remained after clean + ingest dedup)
- idle runs >= 15 frames (action-chunk size used in training)
- per-task length >3 sigma and clear within-family **20-PC Mahalanobis** outliers (retry / thrash / bad seat)
5. Final exact-duplicate scan must be zero or the build aborts.
Quality over quantity: mild multivariate peg shape outliers that look like valid successes were kept.
## Schema
DROID joint-position contract:
| key | shape | notes |
|-----|-------|--------|
| `action` | (8,) | absolute joint targets + gripper |
| `observation.state` | (8,) | joint pos (7) + gripper |
| `observation.joint_vel` | (7,) | rad/s |
| `observation.eef_pos` | (3,) | base-frame XYZ (m) |
| `observation.eef_quat` | (4,) | world WXYZ |
| `observation.images.front` | video HxWx3 | 180x320 |
| `observation.images.wrist` | video HxWx3 | 180x320 |
## Load
```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("hud-evals/AssembleBench")
print(ds.num_episodes, ds.num_frames)
print(ds[0]["observation.images.wrist"].shape)
```
## Intended use
- Supervised fine-tuning of VLA / diffusion / ACT policies with **action chunk size 15**
- Multi-task assembly benchmarking (peg / gear / nut families)
Not a teleop human dataset. Demonstrations are from a privileged scripted expert in Isaac Sim / Isaac Lab; visuals and proprio match the policy observation contract.
## Reference checkpoints
pi0.5 finetunes trained on this dataset:
| Checkpoint | What it is |
|---|---|
| [`pi05-AssembleBench-12k`](https://huggingface.co/hud-evals/pi05-AssembleBench-12k) | behavior-cloning baseline, all 14 tasks |
| [`pi05-AssembleBench-cgdagger-r3`](https://huggingface.co/hud-evals/pi05-AssembleBench-cgdagger-r3) | BC + 3 rounds of code-gated DAgger, the final checkpoint |
## Lineage
- Simulator: NVIDIA Isaac Sim 6 / Isaac Lab Arena
- Format: LeRobot v3.0
|