Search is not available for this dataset
observation.state listlengths 8 8 | action listlengths 7 7 | timestamp float32 0 25.2 | frame_index int64 0 504 | episode_index int64 0 14.1k | index int64 0 2.26M | task_index int64 0 39 |
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End of preview. Expand in Data Studio
libero_plus_bowl5
日本語版: README.ja.md
lerobot/libero_plus (14,347 episodes) with the 5 "black bowl" pick-and-place tasks —
the ones with the highest collision rate against a nearby object during scoring — removed
and replaced by newly-recorded scripted demonstrations that grasp and place more
carefully. Everything else in libero_plus is unchanged and re-used via hardlink (videos
were not re-encoded).
Why these 5 tasks
All 5 involve picking up the black bowl next to or on top of another object, where the gripper has to straddle the bowl's rim (the bowl is wider than the gripper, so it cannot be grasped from directly above):
pick_up_the_black_bowl_next_to_the_cookie_box_and_place_it_on_the_platepick_up_the_black_bowl_on_the_cookie_box_and_place_it_on_the_platepick_up_the_black_bowl_on_the_ramekin_and_place_it_on_the_platepick_up_the_black_bowl_between_the_plate_and_the_ramekin_and_place_it_on_the_platepick_up_the_black_bowl_next_to_the_ramekin_and_place_it_on_the_plate
In the distributed demonstrations, the human demonstrator's grasp radius (wrist 4.0 cm
from the bowl's rotation axis) sits inside the rim wall's actual center (4.71 cm), so one
finger contacts the wall before the other — the bowl gets pushed rather than gripped. This
family accounted for 11.3 of the ~17 percentage points of score lost to unwanted contact
across all LIBERO-plus tasks measured (see composition table below).
Composition
| Part | Episodes | Note |
|---|---|---|
libero_plus minus the 5 bowl tasks |
12,467 | Hardlinked from lerobot/libero_plus, untouched |
| Newly recorded scripted demos, 5 bowl tasks | 1,618 | Replaces the 1,880 removed episodes (see below) |
| Total | 14,085 | 40 tasks, 2,264,704 frames, 20 fps |
The new demos were generated with a scripted rim-grasp policy
(solution/tools/scripted_demo/scripted_demo.py, class RimPickPlace) that grasps at the
rim wall's actual center radius and reduces descent speed before contact, across two
initial-state sources (the evaluator's 50 canonical perturbed poses per task, and 50
poses sampled around each human demonstration's initial pose, for 4 of the 5 tasks) times
a scripted-policy pass. Episodes where the policy did not reach the goal, or touched a
non-target object by more than 1mm, were discarded rather than kept — hence 1,618 kept out
of an attempted ~1,880 (in line with the family's independently-measured contact-free
success rate of 130/150, 86.7%, at grasp offsets up to 9.67 cm — well beyond the ≤1.0 cm
of hand offset present in the original human demonstrations).
Task instruction text was normalized to match the distributed dataset's phrasing (not the raw BDDL text used at scoring time), since the eval harness maps the grader's instruction to one of the training set's exact 40 strings before it reaches the policy — a scripted demo recorded with off-vocabulary phrasing would sit outside that lookup and never be matched at inference time.
Format
LeRobot v3.0 dataset, same feature schema as lerobot/libero_plus:
| Feature | dtype | shape |
|---|---|---|
observation.images.front |
video | [256, 256, 3] |
observation.images.wrist |
video | [256, 256, 3] |
observation.state |
float32 | [8] |
action |
float32 | [7] |
timestamp / frame_index / episode_index / index / task_index |
float32 / int64 | [1] |
Contents
data/chunk-000/...
videos/observation.images.front/...
videos/observation.images.wrist/...
meta/info.json
meta/stats.json
meta/tasks.parquet
meta/episodes/chunk-000/file-000.parquet
bowl5_expected.json # expected episode/frame/task counts, used as a build-time gate
Use
from huggingface_hub import hf_hub_download # or: hf download siruku6/libero_plus_bowl5 --repo-type dataset
from lerobot.datasets.lerobot_dataset import LeRobotDataset
ds = LeRobotDataset("siruku6/libero_plus_bowl5")
Intended use and limitations
- Built for fine-tuning π0.5/LIBERO policies as a drop-in replacement for
lerobot/libero_plus; the only intentional difference from the distributed dataset is the 5 bowl tasks' demonstrations, so it is meant to be compared against a model trained on the unmodifiedlibero_pluswith everything else held fixed. - The scripted replacement demos are simulation-only (LIBERO + simulated Franka Panda via
robosuite/MuJoCo), same as the rest of
libero_plus. - Only the 5 named tasks were touched.
libero_plus_bowl5_initposeextends this by also varying initial robot pose and visual appearance across a larger set of re-recorded episodes (16,820 episodes). - Training-time effect: isolated with an action-expert-only fine-tune, swapping in this
data (vs. unmodified
libero_plus) moved the local Track2 composite score from 0.3656 to 0.3354 (down), concentrated in one held-out camera-perturbation condition. A full-parameter fine-tune on this data scored 0.4004 (best of the four weights compared), but there is no full-parameter-on-unmodified-data control to isolate how much of that is the data vs. the training regime — seedocs/resources/datasets.md§4 for the full comparison table.
License and attribution
Built on the LIBERO benchmark, distributed via lerobot/libero_plus.
| Component | License |
|---|---|
| LIBERO (Copyright (c) 2023 UT-Austin Robot Perception and Learning Lab / Lifelong-Robot-Learning, https://github.com/Lifelong-Robot-Learning/LIBERO) | MIT |
lerobot/libero_plus (unmodified portion, hardlinked here) |
MIT (inherits from LIBERO) |
| Newly recorded scripted demonstrations (this repo's own contribution) | MIT |
| robosuite (https://github.com/ARISE-Initiative/robosuite) | MIT |
| MuJoCo (https://github.com/google-deepmind/mujoco) | Apache-2.0 |
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