MarcelTorne/fold_pants_preferences

fold_pants — pairwise preferences on a Franka Panda

Real-robot trajectories for "fold the shorts" 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	536
Frames	530,494 (~9.8 h at 15 fps)
Preference pairs	1448
Preference labels (pair x axis)	4390
Judgment axes	45
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	399	385498
demo	137	144996

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: A 1996, B 1968, Equal 426.

Axes

axis	labels
Overall quality	1330
Quality of 1st fold	239
Quality of 2nd fold	239
Wrinkle of 2nd fold	239
Alignment of final fold	239
Fast	239
Smooth	239
Wrinkle of 1st fold	239
Damage to environment	238
Quality of 3rd fold	237
Wrinkle of 3rd fold	237
fast	69
wrinkle of first fold	48
speed	47
alignment of final fold	46
wrinkle of second fold	42
first fold quality	39
third fold quality	38
smoothness	32
second fold quality	30
final alignment	28
smooth	28
stable	26
wrinkle of third fold	26
first fold wrinkle	19
final fold alignment	19
quality of second fold	18
stability	16
second fold wrinkle	16
third fold wrinkle	16
quality of third fold	14
stablity	12
environment damage	11
quality of first fold	10
damage to environment	10
destruction to environment	5
environmental damage	2
damage caused to environment	1
Quality of placement of big plate	1
Quality of placement of small plate	1
Quality of placement of cup	1
Quality of placement of cutlery	1
Smoothness / carefulness	1
Speed	1
Formality of setup	1

Pairs by source

source	directory	axis set	pairs
cross	abhijnya/cross_preferences	fixed	61
cross	am208/cross_preferences	fixed	690
cross	am208/cross_preferences_extra	fixed	498
in_session	am208/preferences	fixed	79
in_session	am208/preferences_fold_pants_free	freeform	120

Usage

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("MarcelTorne/fold_pants_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/fold_pants_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.
• Equal is common on some axes. Annotators used it freely; filter deliberately.
• freeform axis 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_id if you need unique pairs.
• Instructions on rollout episodes sometimes carry reward conditioning (e.g. "fold the shorts, 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 in preferences/episodes.parquet.
• No success labels on demo episodes — they are demonstrations, assumed good.

Related datasets

Same robot, same collection pipeline, same schema — four tasks released together:

• MarcelTorne/fold_pants_preferences (fold the shorts) — this dataset
• MarcelTorne/setup_table_preferences — set up the table
• MarcelTorne/put_cube_in_bowl_preferences — put the cube in the bowl
• MarcelTorne/plate_toast_preferences — put the toast in the plate

License

MIT.