MarcelTorne/plate_toast_preferences

plate_toast — pairwise preferences on a Franka Panda

Real-robot trajectories for "put the toast in the plate" 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	271
Frames	83,209 (~1.5 h at 15 fps)
Preference pairs	1491
Preference labels (pair x axis)	2671
Judgment axes	43
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
demo	148	31656
rollout	123	51553

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 1175, A 1131, Equal 365.

Axes

axis	labels
Overall quality	1195
hygiene	140
smoothness	133
speed	120
quality of plating	119
plating quality	108
fast	101
smooth	90
cleanliness	78
destroying the toast	53
toast destruction	44
destruction of the toast	40
placement on the plate	36
clean	35
toast demolishing	34
toast damage	32
stable	27
swift	26
overall quality	23
quickness	22
tidy	21
stability	21
toast deformation	20
environment destruction	16
damaging toast	16
hygienic	16
jerky movements	13
damaging the toast	13
deformation of the toast	13
quick	11
destroying to the toast	8
jerky motion	8
destruction of toast	7
quicker	6
fastness	6
dirtying the table	5
damage to the toast	5
destruction of environment	3
Overall Quality	2
destruction of the environment	2
placement on plate	1
lifting the toast	1
dropping spatula	1

Pairs by source

source	directory	axis set	pairs
cross	abhijnya/cross_preferences_burger	freeform	700
cross	am208/cross_preferences_burger	freeform	730
in_session	am208/preferences_burger	freeform	61

Usage

from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("MarcelTorne/plate_toast_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/plate_toast_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. "put the toast in the plate, 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
• 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) — this dataset

License

MIT.