Datasets:

lucanunz
/

flowsteps

---
license: cc-by-4.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- manipulation
- failure-detection
- franka
configs:
- config_name: default
  data_files: data/*/*.parquet
---
# Paired A/B rollouts of six manipulation policies

4,800 teleoperation-free rollouts of **six** vision-language-action policies on a Franka
Panda, across **four** manipulation tasks. Episodes are organised as **2,400 matched
A/B pairs**: the two arms of a pair run the *same policy* from the *same initial scene* with the
*same seed* for noise sampling, differing only in the number of **Euler (flow-matching) integration steps** used to
sample the action.

## ⚠️ Contains failures: read this before training on it

**2,060 of the 4,800 episodes (43%) are failures**. This is not a dataset of demonstrations.

For application like filtered Behavioural Cloning, filter on the `success` column:

```python
from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata
meta = LeRobotDatasetMetadata("lucanunz/flowsteps")
train = LeRobotDataset("lucanunz/flowsteps", episodes=meta.filter_episodes(lambda ep: ep["success"] == 1))
```

## Episode metadata

Five columns beyond the LeRobot v3 defaults, in `meta/episodes/*.parquet`:

| column | values | meaning |
|---|---|---|
| `policy` | `dit`, `flower`, `lap`, `pi0.5`, `xr-0`, `xvla` | which policy produced the rollout |
| `num_euler_steps` | `1`, `5`, `10` | integration steps used to sample each action — the A/B factor |
| `success` | `0`, `1` | whether the task was accomplished, labelled by a human |
| `pair_index` | `0`–`2399` | the matched pair; identical on both arms |
| `run_number` | `1`, `2` | The run order in the A/B pair (mostly metadata for sanity check, not meaningful for training) |

Plus the v3 natives: `episode_index`, `tasks`, `length`, and per-episode `stats/*`.

### Using the pairs

The two arms of pair *k* are always `episode_index` *2k* and *2k+1*, with the lower
`num_euler_steps` first:

```python
import pandas as pd
eps = meta.episodes.to_pandas()
# success rate per (policy, num_euler_steps)
print(eps.groupby(["policy", "num_euler_steps"])["success"].agg(["mean", "count"]))
# the discordant pairs — one arm succeeded, the other did not.
by_pair = eps.groupby("pair_index")["success"].agg(["min", "max"])
discordant = by_pair[by_pair["min"] != by_pair["max"]].index
print(f"{len(discordant)} discordant pairs of {len(by_pair)}")
```

### `run_number`
Each pair's two arms ran back to back, and the execution order was randomised per pair.
Included only as a sanity check for randomisation.
| | ran first | ran second |
|---|---|---|
| `num_euler_steps == 1` | 1242 | 1158 |
| `num_euler_steps == 5` | 401 | 399 |
| `num_euler_steps == 10` | 757 | 843 |
## The A/B factor
Arm A is **always 1 Euler step**. Arm B is the policy's higher setting and **the value is not the same for every policy**:
| policy | arm A | arm B | episodes |
|---|---|---|---|
| `dit` | 1 step | 10 steps | 800 |
| `flower` | 1 step | 5 steps | 800 |
| `lap` | 1 step | 10 steps | 800 |
| `pi0.5` | 1 step | 10 steps | 800 |
| `xr-0` | 1 step | 5 steps | 800 |
| `xvla` | 1 step | 10 steps | 800 |
## Tasks
| instruction | episodes |
|---|---|
| Close the oven | 1200 |
| Open the drawer | 1200 |
| Open the oven | 1200 |
| Put the spoon in the drawer | 1200 |
## Dataset structure
| | |
|---|---|
| episodes | 4,800 (2,400 pairs × 2 arms) |
| frames | 936,214 |
| duration | 21.7 h at 12 Hz |
| robot | Franka Panda + Robotiq 2F-85 gripper |
| control frequency | 12 Hz |
| cameras | 3 × 224×224 RGB: `image`, `left_ego_image`, `wrist_image` |
| format | LeRobotDataset v3.0 |

### Features

- `observation.state` — `float32[8]`: `[x, y, z, qx, qy, qz, qw, gripper]`
- `action` — `float32[8]`: same layout, the commanded end-effector pose and gripper
- `observation.images.{image,left_ego_image,wrist_image}` — `video`, 224×224 RGB

Poses are the end-effector pose **in the robot base frame**.

The orientation is a **unit quaternion in scalar-last order** (`qx, qy, qz, qw`).
Its sign is **continuous within each episode** (no `q -> -q` jumps between
consecutive frames), with the first frame on the same hemisphere as the home pose
`(1, 0, 0, 0)`, i.e., a 180° rotation about the base x-axis (gripper pointing down).

The gripper channel is **normalised and dimensionless**, and the state and action
channels are not on the same effective scale

| | observed range (all 936,214 frames) | shape |
|---|---|---|
| `observation.state[7]` | `[0.0001, 0.8340]` | measured aperture; never exceeds 0.834 |
| `action[7]` | `[-0.1495, 1.0914]` | commanded, unclipped; 76.1% near 0, 18.7% near 1, 5.2% between |

The action is a binary open/close command that overshoots the nominal `[0, 1]` range, while the state is the aperture actually reached.

### Camera intrinsics

In `meta/robot_profile.json`, which LeRobot v3 has no native slot for. The matrices describe the
**224×224 images as stored**, not the camera's native stream: frames were captured at 424×240
and resized to 224×224 by the policy observation pipeline, and the intrinsics were rescaled to
match. That resize is anisotropic, so `fx != fy` (≈160 vs ≈282) — this is correct and encodes the
non-square pixels, not a calibration error.

## How the episodes were cut

Successful episodes **end at the frame the task was completed.** Rollouts were stopped by a human
keypress and so originally ran on past the moment of success; those trailing frames were located
by frame-level human review and removed. Failed episodes are unmodified.

## Related

The same rollouts will also be published as part of the [Oopsie Data](https://oopsie-data.com) community dataset of robotic manipulation
failures. However, in that release the A/B pairing is lost.

## Citation
If you use this data make sure to cite the related paper
```bibtex
@inproceedings{sujit2026when,
  title={When One Step Is Enough: An Empirical Study of Euler Integration in Flow-Matching Policies},
  author={Sujit, Shivakanth and Nunziante, Luca and Dossa, Rousslan Fernand Julien and Arulkumaran, Kai},
  booktitle={Conference on Robot Learning},
  year={2026},
  organization={PMLR}
}
```

	---
	license: cc-by-4.0
	task_categories:
	- robotics
	tags:
	- LeRobot
	- robotics
	- manipulation
	- failure-detection
	- franka
	configs:
	- config_name: default
	data_files: data//.parquet
	---

	# Paired A/B rollouts of six manipulation policies

	4,800 teleoperation-free rollouts of six vision-language-action policies on a Franka
	Panda, across four manipulation tasks. Episodes are organised as **2,400 matched
	A/B pairs*: the two arms of a pair run the same policy* from the same initial scene with the
	same seed for noise sampling, differing only in the number of Euler (flow-matching) integration steps used to
	sample the action.

	## ⚠️ Contains failures: read this before training on it

	2,060 of the 4,800 episodes (43%) are failures. This is not a dataset of demonstrations.

	For application like filtered Behavioural Cloning, filter on the `success` column:

	```python
	from lerobot.datasets import LeRobotDataset, LeRobotDatasetMetadata

	meta = LeRobotDatasetMetadata("lucanunz/flowsteps")
	train = LeRobotDataset("lucanunz/flowsteps", episodes=meta.filter_episodes(lambda ep: ep["success"] == 1))
	```

	## Episode metadata

	Five columns beyond the LeRobot v3 defaults, in `meta/episodes/*.parquet`:

	\| column \| values \| meaning \|
	\|---\|---\|---\|
	\| `policy` \| `dit`, `flower`, `lap`, `pi0.5`, `xr-0`, `xvla` \| which policy produced the rollout \|
	\| `num_euler_steps` \| `1`, `5`, `10` \| integration steps used to sample each action — the A/B factor \|
	\| `success` \| `0`, `1` \| whether the task was accomplished, labelled by a human \|
	\| `pair_index` \| `0`–`2399` \| the matched pair; identical on both arms \|
	\| `run_number` \| `1`, `2` \| The run order in the A/B pair (mostly metadata for sanity check, not meaningful for training) \|

	Plus the v3 natives: `episode_index`, `tasks`, `length`, and per-episode `stats/*`.

	### Using the pairs

	The two arms of pair k are always `episode_index` 2k and 2k+1, with the lower
	`num_euler_steps` first:

	```python
	import pandas as pd
	eps = meta.episodes.to_pandas()

	# success rate per (policy, num_euler_steps)
	print(eps.groupby(["policy", "num_euler_steps"])["success"].agg(["mean", "count"]))

	# the discordant pairs — one arm succeeded, the other did not.
	by_pair = eps.groupby("pair_index")["success"].agg(["min", "max"])
	discordant = by_pair[by_pair["min"] != by_pair["max"]].index
	print(f"{len(discordant)} discordant pairs of {len(by_pair)}")
	```

	### `run_number`

	Each pair's two arms ran back to back, and the execution order was randomised per pair.
	Included only as a sanity check for randomisation.

	\| \| ran first \| ran second \|
	\|---\|---\|---\|
	\| `num_euler_steps == 1` \| 1242 \| 1158 \|
	\| `num_euler_steps == 5` \| 401 \| 399 \|
	\| `num_euler_steps == 10` \| 757 \| 843 \|

	## The A/B factor

	Arm A is always 1 Euler step. Arm B is the policy's higher setting and the value is not the same for every policy:

	\| policy \| arm A \| arm B \| episodes \|
	\|---\|---\|---\|---\|
	\| `dit` \| 1 step \| 10 steps \| 800 \|
	\| `flower` \| 1 step \| 5 steps \| 800 \|
	\| `lap` \| 1 step \| 10 steps \| 800 \|
	\| `pi0.5` \| 1 step \| 10 steps \| 800 \|
	\| `xr-0` \| 1 step \| 5 steps \| 800 \|
	\| `xvla` \| 1 step \| 10 steps \| 800 \|

	## Tasks

	\| instruction \| episodes \|
	\|---\|---\|
	\| Close the oven \| 1200 \|
	\| Open the drawer \| 1200 \|
	\| Open the oven \| 1200 \|
	\| Put the spoon in the drawer \| 1200 \|

	## Dataset structure

	\| \| \|
	\|---\|---\|
	\| episodes \| 4,800 (2,400 pairs × 2 arms) \|
	\| frames \| 936,214 \|
	\| duration \| 21.7 h at 12 Hz \|
	\| robot \| Franka Panda + Robotiq 2F-85 gripper \|
	\| control frequency \| 12 Hz \|
	\| cameras \| 3 × 224×224 RGB: `image`, `left_ego_image`, `wrist_image` \|
	\| format \| LeRobotDataset v3.0 \|

	### Features

	- `observation.state` — `float32[8]`: `[x, y, z, qx, qy, qz, qw, gripper]`
	- `action` — `float32[8]`: same layout, the commanded end-effector pose and gripper
	- `observation.images.{image,left_ego_image,wrist_image}` — `video`, 224×224 RGB

	Poses are the end-effector pose in the robot base frame.

	The orientation is a unit quaternion in scalar-last order (`qx, qy, qz, qw`).
	Its sign is continuous within each episode (no `q -> -q` jumps between
	consecutive frames), with the first frame on the same hemisphere as the home pose
	`(1, 0, 0, 0)`, i.e., a 180° rotation about the base x-axis (gripper pointing down).

	The gripper channel is normalised and dimensionless, and the state and action
	channels are not on the same effective scale

	\| \| observed range (all 936,214 frames) \| shape \|
	\|---\|---\|---\|
	\| `observation.state[7]` \| `[0.0001, 0.8340]` \| measured aperture; never exceeds 0.834 \|
	\| `action[7]` \| `[-0.1495, 1.0914]` \| commanded, unclipped; 76.1% near 0, 18.7% near 1, 5.2% between \|

	The action is a binary open/close command that overshoots the nominal `[0, 1]` range, while the state is the aperture actually reached.

	### Camera intrinsics

	In `meta/robot_profile.json`, which LeRobot v3 has no native slot for. The matrices describe the
	224×224 images as stored, not the camera's native stream: frames were captured at 424×240
	and resized to 224×224 by the policy observation pipeline, and the intrinsics were rescaled to
	match. That resize is anisotropic, so `fx != fy` (≈160 vs ≈282) — this is correct and encodes the
	non-square pixels, not a calibration error.

	## How the episodes were cut

	Successful episodes end at the frame the task was completed. Rollouts were stopped by a human
	keypress and so originally ran on past the moment of success; those trailing frames were located
	by frame-level human review and removed. Failed episodes are unmodified.

	## Related

	The same rollouts will also be published as part of the [Oopsie Data](https://oopsie-data.com) community dataset of robotic manipulation
	failures. However, in that release the A/B pairing is lost.

	## Citation
	If you use this data make sure to cite the related paper
	```bibtex
	@inproceedings{sujit2026when,
	title={When One Step Is Enough: An Empirical Study of Euler Integration in Flow-Matching Policies},
	author={Sujit, Shivakanth and Nunziante, Luca and Dossa, Rousslan Fernand Julien and Arulkumaran, Kai},
	booktitle={Conference on Robot Learning},
	year={2026},
	organization={PMLR}
	}
	```