Search is not available for this dataset
observation.state list | observation.ee list | action list | timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
setup_table — pairwise preferences on a Franka Panda
Real-robot trajectories for "set up the table" with human pairwise preference labels on multiple judgment axes. Built for reward-model / preference-learning research: every label is a comparison of two trajectories on one named axis, not a scalar score.
The trajectory data is a standard LeRobot v2.1 dataset, so it also loads directly as an imitation-learning dataset.
Contents
| Episodes | 467 |
| Frames | 197,896 (~3.7 h at 15 fps) |
| Preference pairs | 1648 |
| Preference labels (pair x axis) | 4432 |
| Judgment axes | 9 |
| Distinct instructions | 1 |
| Cameras | agent_view (third-person), wrist — both 224x224 |
| Robot | Franka Panda, 7-DoF joint control + gripper |
Episodes by kind
| kind | episodes | frames |
|---|---|---|
| rollout | 316 | 133031 |
| demo | 151 | 64865 |
demo episodes are human teleoperated demonstrations. rollout episodes are policy
rollouts recorded in preference-collection sessions; those are the ones that vary in
quality, which is what makes the comparisons informative.
Preference labels
preferences/pairs.parquet — one row per (pair, axis):
| column | meaning |
|---|---|
pair_id |
unique id of the comparison |
episode_index_a, episode_index_b |
index into this LeRobot dataset |
episode_id_a, episode_id_b |
original source trajectory id |
axis |
the judgment axis being compared |
winner |
A, B, or Equal |
source |
in_session (the two rollouts of one session) or cross (arbitrary pair) |
source_dir, source_file |
provenance of the annotation |
annotator |
which annotator's label set this row comes from |
axis_set |
fixed (curated rubric reused across sessions) or freeform (annotator-invented axis names) |
overall_score_a/b |
1–4 Likert quality rating, where the annotator gave one (else null) |
succeeded_a/b |
task-success flag recorded at collection time, where available (else null) |
instruction |
instruction string logged with the comparison |
preferences/pairs.jsonl is the same data with the axes nested per pair.
preferences/episodes.parquet maps episode_index to the source trajectory.
Winner distribution: B 1894, A 1790, Equal 748.
Axes
| axis | labels |
|---|---|
Overall quality |
1648 |
Quality of placement of big plate |
348 |
Quality of placement of small plate |
348 |
Quality of placement of cup |
348 |
Quality of placement of cutlery |
348 |
Smoothness / carefulness |
348 |
Speed |
348 |
Formality of setup |
348 |
Damage to environment |
348 |
Pairs by source
| source | directory | axis set | pairs |
|---|---|---|---|
| cross | abhijnya/cross_preferences_setup |
fixed | 800 |
| cross | am208/cross_preferences_setup |
fixed | 690 |
| in_session | am208/preferences_setup |
fixed | 158 |
Usage
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("MarcelTorne/setup_table_preferences")
print(ds[0].keys())
# train a reward model on the pairwise labels
import pandas as pd
from huggingface_hub import hf_hub_download
prefs = pd.read_parquet(hf_hub_download("MarcelTorne/setup_table_preferences", "preferences/pairs.parquet",
repo_type="dataset"))
decisive = prefs[prefs.winner != "Equal"] # drop ties
overall = decisive[decisive.axis.str.lower().str.contains("overall")]
Both cameras are dtype: video. observation.state is
[joint_0..joint_6, gripper], observation.ee is [x, y, z, roll, pitch, yaw],
and action is the commanded [joint_position(7), gripper_position(1)].
Caveats
- Frames are h264 (crf 16), not bit-exact. The source HDF5 held raw uint8 frames; re-encoding at the same 224x224 resolution shrinks the release ~100x at 41.8 dB PSNR (mean absolute error 1.1/255, 99th pct 7/255), measured on fold_pants cloth texture. Do not expect byte-level reproduction of the originals.
Equalis common on some axes. Annotators used it freely; filter deliberately.freeformaxis names are not a closed vocabulary. They were invented per session, so the same idea appears under several names (fast/speed,smooth/smoothness). Normalise before aggregating across sessions.- A pair can be labelled by more than one annotator. The two annotators' cross-pair
sets are near-disjoint, but not perfectly; group by
pair_idif you need unique pairs. - Instructions on
rolloutepisodes sometimes carry reward conditioning (e.g."set up the table, fast: 1.0, ...") because the policy that produced them was reward-conditioned. The LeRobot task string is the plain instruction; the logged string is kept inpreferences/episodes.parquet. - No success labels on
demoepisodes — they are demonstrations, assumed good.
Related datasets
Same robot, same collection pipeline, same schema — four tasks released together:
MarcelTorne/fold_pants_preferences— fold the shortsMarcelTorne/setup_table_preferences(set up the table) — this datasetMarcelTorne/put_cube_in_bowl_preferences— put the cube in the bowlMarcelTorne/plate_toast_preferences— put the toast in the plate
License
MIT.
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