README.md

metadata

license: mit
task_categories:
  - robotics
tags:
  - LeRobot
  - robotics
  - preference-learning
  - reward-modelling
  - rlhf
  - manipulation
  - franka
configs:
  - config_name: default
    data_files: data/*/*.parquet

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.
• 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. "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 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) — this dataset
• MarcelTorne/put_cube_in_bowl_preferences — put the cube in the bowl
• MarcelTorne/plate_toast_preferences — put the toast in the plate

License

MIT.