README.md

---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
task_categories:
  - robotics
tags:
  - LeRobot
  - franka
  - fr3
  - manipulation
  - cube-stacking
  - synthetic
  - generated
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files: data/chunk-*/file-*.parquet
---

# Franka cube stacking — generated trajectories with IDM pseudo-actions

**7,232 action-labelled manipulation trajectories, 2.40 M frames, 66.7 hours at 10 Hz.**
Every frame in this dataset was generated by a video model. No robot was moved to produce it.

A Cosmos-Predict2 LoRA imagines a Franka FR3 stacking three cubes; a fine-tuned inverse
dynamics model watches the imagined video and recovers the actions that would produce it.
No human selected any sample at any stage — trajectories were kept or discarded by an
automatic filter.

Conforms to the Physical Data Engine `pde/training-dataset@v1` output contract, in LeRobot V3
layout. Read [§ Deviations](#deviations-from-the-contract) before training on it.

## Contents

| | |
|---|---|
| episodes | 7,232 |
| frames | 2,401,024 (66.7 h) |
| rate | exactly 10 Hz, 332 frames per episode |
| tasks | 6 — every ordering of three cubes |
| views | `third_person_0`, `third_person_1`, `wrist` — 320×180 RGB |
| size | 14 GB |

Each episode is a four-subtask sequence: pick A → stack A on B → pick C → stack C on AB.

```
action.eef_delta_metric        float32[7]  [dx_m, dy_m, dz_m, drx_rad, dry_rad, drz_rad, gripper_closed]
action.eef_delta_metric_valid  bool[7]     per-dimension validity
```

Translation is metres per 0.1 s step in the `robot_base` frame for the `fr3_hand` body.
Gripper is `0 = open`, `1 = closed`. The final row of every episode carries an all-false
validity mask — no action is fabricated for a frame with no successor.

## Loading

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("finde159/DataEngine_NT")
```

Or directly:

```python
import pandas as pd
df = pd.read_parquet("hf://datasets/finde159/DataEngine_NT/data/chunk-000/file-000.parquet")
```

Videos are concatenated per file; `meta/episodes/` gives each episode's window as
`from_timestamp` / `to_timestamp` within its video file.

## How it was made

1. **Scene variants.** A held-out camera frame is edited to move the cubes. Only the left
   view is edited by hand; the right view is *derived* by fitting each cube as a rigid 5 cm
   cuboid and projecting it through the fixed stereo geometry, so both views stay
   geometrically consistent.
2. **Generation.** For each of 20 layouts × 6 cube orderings, a tree of clips is generated
   3 seeds wide and 4 subtasks deep — 120 clips yielding 81 complete trajectories per tree.
   Each clip is conditioned on the previous clip's last frame.
3. **Labelling.** An inverse dynamics model fine-tuned on 16 real teleoperated
   demonstrations reads each trajectory and emits actions. It sees two frames 16 apart and
   predicts a 16-step horizon, run at stride 1, so each frame is covered by up to 16
   overlapping predictions.
4. **Filtering.** Three rules on the gripper signal, per subtask quarter. Thresholds derived
   from **real held-out demonstrations**, never tuned on generated video.

   - **C1** exactly one gripper transition per quarter, in the direction the subtask implies
   - **C2** that transition falls at 0.30–0.93 of its quarter
   - **C3** at least 50% of the quarter's frames are unanimous across the 16-window ensemble

**9,720 trajectories were generated; 7,232 (74.4%) passed and are published here.** Pass rate
declines along the chain — 0.99 / 0.92 / 0.86 / 0.84 for subtasks 1–4 — because each clip is
conditioned on the previous one's final frame, so error accumulates.

Models: [Cosmos-Predict2 LoRA and the fine-tuned IDM](https://huggingface.co/finde159/NT_checkpoint).

## Deviations from the contract

Both were raised with the receiving team and waived. They are recorded here, and in
`meta/pde_output_contract.json`, so nobody has to rediscover them.

**The action is realised motion, not the issued command.** The contract asks for
`desired_relative_target` / `command_at_t`. These values are the IDM's estimate of the motion
between observations. On the source rig the realised motion tracks the recorded human command
at about 90% (1.824 m of path against 2.002 m commanded), so the two are close but not the
same quantity. For generated video no human command exists at all. The real command channel is
preserved in the native archives.

**No camera calibration exists.** The contract requires intrinsics, extrinsics and a frame
graph. This rig has none, and these views are synthetic anyway — no physical camera was ever
calibrated. `meta/calibration.json` states this rather than inventing values.

## Things that will surprise you

**The source recordings are mislabelled 60 Hz.** All 20 demonstrations measure **41.68 Hz**
(sd 0.35), and every downstream fps label inherited the error — the intermediate datasets are
tagged 15 Hz while carrying 10.42 Hz of content. This dataset corrects it: the video is
retimed to real time *before* resampling, so 10 Hz here means 10 Hz, and 332 frames really is
33.2 seconds.

**Rotation is identically zero, and that is intentional.** The teleoperator never rotated the
controller while clutched — the angular command is zero on every recorded row of every demo.
These are not unknown dimensions filled with zero; they are a recorded hold.

**The gripper label leads visible finger motion by roughly 20 frames** in the source data
(one frame of convention plus physical actuation lag). A visual open/closed detector will
disagree with these labels systematically; the labels match the robot's control input.

**`fr3_hand` vs the TCP.** The source records the gripper TCP, offset `[0, 0, 0.1034] m` from
the flange (measured by forward kinematics). Converting a TCP delta to a hand delta needs
`d_hand = d_tcp − (R(t+1) − R(t)) @ offset`; because the rotation is constant here that term
is exactly zero, so the translation deltas are already `fr3_hand` deltas. The export asserts
that precondition per episode rather than assuming it.

## Limitations

- **One task, one scene.** Three 5 cm cubes on a white table, a fixed camera rig, an FR3 arm.
- **The filter checks label coherence, not task success.** All three rules read the IDM's own
  output. A clip where the arm closes on empty air with correct timing passes. Hand
  inspection is the only check on physical plausibility.
- **Generated video degrades late in an episode.** Roughly 16% of fourth subtasks fail the
  structure check; artefacts accumulate along the conditioning chain.
- **No held-out split.** The video model was trained on 19 demonstrations with no holdout, so
  do not evaluate against those demonstrations expecting generalisation.

## Licence

Derived from NVIDIA Cosmos-Predict2 weights under the NVIDIA Open Model Licence, and from
NVIDIA's GR00T-Dreams IDM. The licence position on model *outputs* differs from redistributing
weights; this dataset is gated for that reason. Attribution to NVIDIA is required.