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MarcelTorne
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fold_pants_preferences

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Tags:
LeRobot
robotics
preference-learning
reward-modelling
rlhf
manipulation
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Dask
Polars
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  1. README.md +187 -0
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  39. export_summary.json +22 -0
  40. meta/episodes.jsonl +536 -0
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  42. meta/info.json +151 -0
  43. meta/tasks.jsonl +1 -0
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README.md ADDED
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+ ---
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+ license: mit
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+ task_categories:
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+ - robotics
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+ tags:
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+ - LeRobot
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+ - robotics
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+ - preference-learning
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+ - reward-modelling
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+ - rlhf
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+ - manipulation
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+ - franka
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+ configs:
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+ - config_name: default
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+ data_files: data/*/*.parquet
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+ ---
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+
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+ # fold_pants — pairwise preferences on a Franka Panda
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+
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+ Real-robot trajectories for **"fold the shorts"** with **human pairwise preference
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+ labels on multiple judgment axes**. Built for reward-model / preference-learning
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+ research: every label is a comparison of two trajectories on one named axis, not a
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+ scalar score.
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+
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+ The trajectory data is a standard [LeRobot](https://github.com/huggingface/lerobot)
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+ v2.1 dataset, so it also loads directly as an imitation-learning dataset.
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+
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+ ## Contents
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+
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+ | | |
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+ |---|---|
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+ | Episodes | **536** |
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+ | Frames | **530,494** (~9.8 h at 15 fps) |
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+ | Preference pairs | **1448** |
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+ | Preference labels (pair x axis) | **4390** |
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+ | Judgment axes | **45** |
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+ | Distinct instructions | **1** |
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+ | Cameras | `agent_view` (third-person), `wrist` — both 224x224 |
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+ | Robot | Franka Panda, 7-DoF joint control + gripper |
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+
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+ ### Episodes by kind
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+
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+ | kind | episodes | frames |
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+ |---|---|---|
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+ | rollout | 399 | 385498 |
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+ | demo | 137 | 144996 |
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+
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+ `demo` episodes are human teleoperated demonstrations. `rollout` episodes are policy
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+ rollouts recorded in preference-collection sessions; those are the ones that vary in
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+ quality, which is what makes the comparisons informative.
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+
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+ ## Preference labels
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+
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+ `preferences/pairs.parquet` — one row per (pair, axis):
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+
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+ | column | meaning |
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+ |---|---|
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+ | `pair_id` | unique id of the comparison |
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+ | `episode_index_a`, `episode_index_b` | index into this LeRobot dataset |
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+ | `episode_id_a`, `episode_id_b` | original source trajectory id |
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+ | `axis` | the judgment axis being compared |
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+ | `winner` | `A`, `B`, or `Equal` |
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+ | `source` | `in_session` (the two rollouts of one session) or `cross` (arbitrary pair) |
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+ | `source_dir`, `source_file` | provenance of the annotation |
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+ | `annotator` | which annotator's label set this row comes from |
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+ | `axis_set` | `fixed` (curated rubric reused across sessions) or `freeform` (annotator-invented axis names) |
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+ | `overall_score_a/b` | 1–4 Likert quality rating, where the annotator gave one (else null) |
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+ | `succeeded_a/b` | task-success flag recorded at collection time, where available (else null) |
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+ | `instruction` | instruction string logged with the comparison |
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+
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+ `preferences/pairs.jsonl` is the same data with the axes nested per pair.
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+ `preferences/episodes.parquet` maps `episode_index` to the source trajectory.
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+
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+ Winner distribution: **A** 1996, **B** 1968, **Equal** 426.
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+
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+ ### Axes
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+
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+ | axis | labels |
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+ |---|---|
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+ | `Overall quality` | 1330 |
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+ | `Quality of 1st fold` | 239 |
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+ | `Quality of 2nd fold` | 239 |
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+ | `Wrinkle of 2nd fold` | 239 |
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+ | `Alignment of final fold` | 239 |
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+ | `Fast` | 239 |
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+ | `Smooth` | 239 |
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+ | `Wrinkle of 1st fold` | 239 |
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+ | `Damage to environment` | 238 |
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+ | `Quality of 3rd fold` | 237 |
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+ | `Wrinkle of 3rd fold` | 237 |
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+ | `fast` | 69 |
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+ | `wrinkle of first fold` | 48 |
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+ | `speed` | 47 |
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+ | `alignment of final fold` | 46 |
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+ | `wrinkle of second fold` | 42 |
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+ | `first fold quality` | 39 |
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+ | `third fold quality` | 38 |
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+ | `smoothness` | 32 |
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+ | `second fold quality` | 30 |
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+ | `final alignment` | 28 |
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+ | `smooth` | 28 |
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+ | `stable` | 26 |
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+ | `wrinkle of third fold` | 26 |
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+ | `first fold wrinkle` | 19 |
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+ | `final fold alignment` | 19 |
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+ | `quality of second fold` | 18 |
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+ | `stability` | 16 |
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+ | `second fold wrinkle` | 16 |
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+ | `third fold wrinkle` | 16 |
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+ | `quality of third fold` | 14 |
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+ | `stablity` | 12 |
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+ | `environment damage` | 11 |
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+ | `quality of first fold` | 10 |
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+ | `damage to environment` | 10 |
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+ | `destruction to environment` | 5 |
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+ | `environmental damage` | 2 |
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+ | `damage caused to environment` | 1 |
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+ | `Quality of placement of big plate` | 1 |
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+ | `Quality of placement of small plate` | 1 |
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+ | `Quality of placement of cup` | 1 |
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+ | `Quality of placement of cutlery` | 1 |
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+ | `Smoothness / carefulness` | 1 |
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+ | `Speed` | 1 |
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+ | `Formality of setup` | 1 |
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+
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+ ### Pairs by source
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+
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+ | source | directory | axis set | pairs |
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+ |---|---|---|---|
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+ | cross | `abhijnya/cross_preferences` | fixed | 61 |
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+ | cross | `am208/cross_preferences` | fixed | 690 |
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+ | cross | `am208/cross_preferences_extra` | fixed | 498 |
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+ | in_session | `am208/preferences` | fixed | 79 |
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+ | in_session | `am208/preferences_fold_pants_free` | freeform | 120 |
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+
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+ ## Usage
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+
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+ ```python
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+ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
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+ ds = LeRobotDataset("MarcelTorne/fold_pants_preferences")
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+ print(ds[0].keys())
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+ ```
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+
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+ ```python
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+ # train a reward model on the pairwise labels
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+ import pandas as pd
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+ from huggingface_hub import hf_hub_download
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+ prefs = pd.read_parquet(hf_hub_download("MarcelTorne/fold_pants_preferences", "preferences/pairs.parquet",
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+ repo_type="dataset"))
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+ decisive = prefs[prefs.winner != "Equal"] # drop ties
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+ overall = decisive[decisive.axis.str.lower().str.contains("overall")]
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+ ```
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+
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+ Both cameras are `dtype: video`. `observation.state` is
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+ `[joint_0..joint_6, gripper]`, `observation.ee` is `[x, y, z, roll, pitch, yaw]`,
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+ and `action` is the commanded `[joint_position(7), gripper_position(1)]`.
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+
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+ ## Caveats
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+
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+ - **Frames are h264 (crf 16), not bit-exact.** The source HDF5 held raw uint8
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+ frames; re-encoding at the same 224x224 resolution shrinks the release ~100x at
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+ 41.8 dB PSNR (mean absolute error 1.1/255, 99th pct 7/255), measured on
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+ fold_pants cloth texture. Do not expect byte-level reproduction of the originals.
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+ - **`Equal` is common on some axes.** Annotators used it freely; filter deliberately.
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+ - **`freeform` axis names are not a closed vocabulary.** They were invented per
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+ session, so the same idea appears under several names (`fast` / `speed`,
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+ `smooth` / `smoothness`). Normalise before aggregating across sessions.
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+ - **A pair can be labelled by more than one annotator.** The two annotators' cross-pair
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+ sets are near-disjoint, but not perfectly; group by `pair_id` if you need unique pairs.
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+ - **Instructions on `rollout` episodes sometimes carry reward conditioning** (e.g.
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+ `"fold the shorts, fast: 1.0, ..."`) because the policy that produced them was
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+ reward-conditioned. The LeRobot task string is the plain instruction; the logged
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+ string is kept in `preferences/episodes.parquet`.
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+ - **No success labels on `demo` episodes** — they are demonstrations, assumed good.
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+
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+ ## Related datasets
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+
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+ Same robot, same collection pipeline, same schema — four tasks released together:
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+
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+ - **`MarcelTorne/fold_pants_preferences`** (fold the shorts) — this dataset
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+ - [`MarcelTorne/setup_table_preferences`](https://huggingface.co/datasets/MarcelTorne/setup_table_preferences) — set up the table
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+ - [`MarcelTorne/put_cube_in_bowl_preferences`](https://huggingface.co/datasets/MarcelTorne/put_cube_in_bowl_preferences) — put the cube in the bowl
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+ - [`MarcelTorne/plate_toast_preferences`](https://huggingface.co/datasets/MarcelTorne/plate_toast_preferences) — put the toast in the plate
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+
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+ ## License
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+
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+ MIT.
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