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Pepijn a07f22e22c feat(envs): add LIBERO-plus robustness benchmark (#3313)
* feat(envs): add LIBERO-plus robustness benchmark integration

- LiberoPlusEnv config (subclass of LiberoEnv, same gym interface)
- Docker image installing LIBERO-plus fork via PYTHONPATH
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_libero_plus
- pyproject.toml: libero_plus extra

* fix(libero): use suite's perturbation-aware init_states loader

LIBERO-plus's Benchmark class exposes a `get_task_init_states(i)` method that
strips perturbation suffixes (`_table_N`, `_tb_N`, `_view_`, `_language_`,
`_light_`, `_add_`, `_level`) and loads the underlying base `.pruned_init`
file — the on-disk name for a perturbation variant doesn't exist as a file,
only the base does. lerobot's loader was bypassing that logic and trying to
read the suffix-bearing filename directly, which failed for every non-zero
task id and killed the eval before any rollout video could be written.

Delegate to the suite's method when it exists; fall back to the path-based
loader for vanilla LIBERO (which does not provide the method).

Also drop the hf-libero install + init_files copy from the LIBERO-plus
Dockerfile — the LIBERO-plus clone already ships both `bddl_files/` and
`init_files/` for all five suites, so the copy was unnecessary and the
`cp -r` into an existing dir produced a confusing nested layout.

* fix(libero): resolve LIBERO-plus perturbation init_states path ourselves

Delegating to `task_suite.get_task_init_states(i)` works for path resolution
but LIBERO-plus's method calls `torch.load(path)` without `weights_only=False`,
which fails on PyTorch 2.6+ because the pickled init_states contains numpy
objects not in the default allowlist:

    _pickle.UnpicklingError: Weights only load failed.
    WeightsUnpickler error: Unsupported global:
      GLOBAL numpy.core.multiarray._reconstruct was not an allowed global.

Mirror LIBERO-plus's suffix-stripping logic (`_table_N`, `_tb_N`, `_view_`,
`_language_`, `_light_`, `_add_`, `_level`) in our own helper so we can pass
`weights_only=False` ourselves. Vanilla LIBERO task names don't contain any
of these patterns except for `_table_` when followed by the word `center`
(e.g. `pick_up_the_black_bowl_from_table_center_...`), and the regex
requires `_table_\\d+` so semantic uses are preserved.

* fix(libero-plus): download perturbation assets from Sylvest/LIBERO-plus

LIBERO-plus's bddl_base_domain.py resolves scene XMLs with
`os.path.join(DIR_PATH, "../assets")`, so the `assets` key in config.yaml
has no effect on scene lookup — MuJoCo always opens
`<clone>/libero/libero/assets/scenes/...`. With no such directory present,
every perturbation task fails on:

    FileNotFoundError: No such file or directory:
      .../libero-plus/libero/libero/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml

These textures, views, and extra objects ship only in the 6.4 GB `assets.zip`
published at `Sylvest/LIBERO-plus` (the LIBERO-plus README explicitly says
to download and unzip it into the package dir). Fetch it via `hf_hub_download`,
unzip into `${LIBERO_PLUS_ROOT}/`, install `unzip`, and point config.yaml at
the extracted dir so everything stays consistent. The download lives in its
own Docker layer so subsequent rebuilds reuse the cached assets.

Drops the lerobot/libero-assets snapshot_download — that mirror only has
vanilla LIBERO textures and is ignored for scene loading anyway.

* fix(libero-plus): flatten deep path prefix from Sylvest/LIBERO-plus assets.zip

The 6.4 GB zip ships with every entry prefixed by
`inspire/hdd/project/embodied-multimodality/public/syfei/libero_new/release/dataset/LIBERO-plus-0/assets/...`
(the author's internal filesystem layout, not the layout the LIBERO-plus
README promises), so the previous `unzip -d ${LIBERO_PLUS_ROOT}/` created
`${LIBERO_PLUS_ROOT}/inspire/.../assets/` — robosuite still opened
`${LIBERO_PLUS_ROOT}/assets/scenes/tabletop_table_Cobblestone01_GLOSS_6K.xml`
and hit the same FileNotFoundError.

Extract to a scratch dir, then `mv` the nested `assets/` subtree to the
expected location. Verified the target file exists in the zip central
directory under that exact prefix.

* refactor(libero): inline init_states resolver behind single regex

Collapse the three-style suffix stripper (split/re.sub/in) into one
compiled regex, drop the (Path, bool) tuple return, and move the
`_add_`/`_level` reshape branch into the caller so each branch loads
its own file and returns directly. Net: -11 lines, one fewer helper.

* refactor(libero-plus): rebase docker image on huggingface/lerobot-gpu

Mirror the libero/metaworld/robomme pattern: start from the nightly GPU
image (apt deps, python, uv, venv, lerobot[all] already there) and only
layer on what LIBERO-plus uniquely needs — its wand/ImageMagick build
deps, the non-extra runtime pips (robosuite==1.4.1, bddl, …), the
PYTHONPATH-shadowed fork, and the 6.4 GB assets.zip.

Drops ~50 lines of duplicated base setup (CUDA FROM, apt python, uv
install, user creation, venv init) the nightly already provides.
123 → 73 lines.

Also:
- Add libero_plus to docs/source/_toctree.yml under Benchmarks so
  doc-builder's TOC integrity check stops failing.
- Repoint the docs dataset link from pepijn223/libero_plus_lerobot to
  the canonical lerobot/libero_plus.
- Revert the stray uv.lock churn (revision/marker diff that crept in
  from an unrelated resolve — unrelated to LIBERO-plus).

* fix(libero-plus): stop touching pyproject + uv.lock

The fast-tests job was rejecting the branch because pyproject.toml had a
[libero_plus] extra whose git dep wasn't represented in uv.lock.

The Docker image no longer needs the extra — it clones LIBERO-plus
directly and PYTHONPATH-shadows hf-libero. Drop [libero_plus] from
pyproject and restore pyproject.toml + uv.lock to exactly what's on
origin/main, so `uv sync --locked --extra test` is a no-op for this PR.

Also repoint the doc/CI/env comments that still mentioned the extra at
the Docker install path.

* fix(libero-plus): strip perturbation metadata from task descriptions

LIBERO-plus builds task.language by space-joining the perturbation-variant
filename, so every non-_language_ variant inherits a trailing blob like
"view 0 0 100 0 0 initstate 0 noise 45" or "add 16". That shows up in the
dashboard video labels and no longer matches the base instruction stored
in the training dataset.

Strip those tokens in extract_task_descriptions.py with an end-anchored
regex over the {view,initstate,noise,add,tb,table,light,level}(+digits)
vocabulary. The anchor preserves mid-sentence literal uses of those words
(e.g. "from table center and place it on the plate") — only the trailing
metadata chain is removed. _language_ variants carry real BDDL-sourced
text and are left untouched.

* ci: point benchmark eval checkpoints at the lerobot/ org mirrors

pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in
this branch (libero, metaworld, and the per-branch benchmark). The
checkpoints were mirrored into the lerobot/ org and that's the canonical
location going forward.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: integrate PR #3313 review feedback

- docs: fix paper link to arxiv, add benchmark image, add suite descriptions,
  add LIBERO-plus replacement warning, restructure eval section to match
  LIBERO doc style, fix policy I/O section, remove false try/except claim
- docker: fix shell grouping for hf-libero uninstall, replace hardcoded
  asset path with dynamic find
- ci: add Docker Hub login step, add HF_USER_TOKEN guard on eval step
- envs: add is_libero_plus param to get_task_init_states so vanilla LIBERO
  always takes the simple path

* fix(docs): use correct LIBERO-plus teaser image URL

* ci(libero-plus): drop redundant hf auth login step

The standalone login step ran `hf auth login` in a throwaway
`docker run --rm` container, so no credentials persisted. Auth is
already performed inside the eval step's container. Removing the
redundant step per PR #3313 review feedback.

* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs

Port of #3416 onto this branch. Without these attributes eval crashes
when calling `env.unwrapped.metadata["render_fps"]` with async vector
envs. Adds `metadata` / `unwrapped` to `_LazyAsyncVectorEnv` and
caches the metadata alongside obs/action spaces in the LIBERO and
MetaWorld factories.

* ci: gate Docker Hub login on secret availability

Fork PRs cannot access `secrets.DOCKERHUB_LEROBOT_{USERNAME,PASSWORD}`,
which made every benchmark job fail at the login step before any of
the actual build/eval work could run. Gate the login on the env-var
expansion of the username so the step is skipped (not failed) when
secrets are absent. Mirrors the existing pattern in the VLABench job.

* Update .github/workflows/benchmark_tests.yml

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* Update scripts/ci/extract_task_descriptions.py

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* Update .github/workflows/benchmark_tests.yml

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* Update docker/Dockerfile.benchmark.libero_plus

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* Update .github/workflows/benchmark_tests.yml

Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>

* fix(libero-plus): address review feedback

* ci(libero-plus): fix YAML indentation in upload-artifact steps

The `uses:` key on two upload-artifact steps was at column 0 instead
of nested under the step, causing `pre-commit run check-yaml` to fail
with "expected <block end>, but found '<block mapping start>'".


Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com>
Co-authored-by: Khalil Meftah <khalil.meftah@huggingface.co>
2026-04-20 21:07:21 +02:00

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- sections:
- local: index
title: LeRobot
- local: installation
title: Installation
title: Get started
- sections:
- local: il_robots
title: Imitation Learning for Robots
- local: bring_your_own_policies
title: Bring Your Own Policies
- local: integrate_hardware
title: Bring Your Own Hardware
- local: hilserl
title: Train a Robot with RL
- local: hilserl_sim
title: Train RL in Simulation
- local: multi_gpu_training
title: Multi GPU training
- local: hil_data_collection
title: Human In the Loop Data Collection
- local: peft_training
title: Training with PEFT (e.g., LoRA)
- local: rename_map
title: Using Rename Map and Empty Cameras
title: "Tutorials"
- sections:
- local: lerobot-dataset-v3
title: Using LeRobotDataset
- local: porting_datasets_v3
title: Porting Large Datasets
- local: using_dataset_tools
title: Using the Dataset Tools
- local: dataset_subtask
title: Using Subtasks in the Dataset
- local: streaming_video_encoding
title: Streaming Video Encoding
title: "Datasets"
- sections:
- local: act
title: ACT
- local: smolvla
title: SmolVLA
- local: pi0
title: π₀ (Pi0)
- local: pi0fast
title: π₀-FAST (Pi0Fast)
- local: pi05
title: π₀.₅ (Pi05)
- local: groot
title: NVIDIA GR00T N1.5
- local: xvla
title: X-VLA
- local: multi_task_dit
title: Multitask DiT Policy
- local: walloss
title: WALL-OSS
title: "Policies"
- sections:
- local: sarm
title: SARM
title: "Reward Models"
- sections:
- local: async
title: Use Async Inference
- local: rtc
title: Real-Time Chunking (RTC)
title: "Inference"
- sections:
- local: envhub
title: Environments from the Hub
- local: envhub_leisaac
title: Control & Train Robots in Sim (LeIsaac)
title: "Simulation"
- sections:
- local: adding_benchmarks
title: Adding a New Benchmark
- local: libero
title: LIBERO
- local: libero_plus
title: LIBERO-plus
- local: metaworld
title: Meta-World
- local: robotwin
title: RoboTwin 2.0
- local: robocasa
title: RoboCasa365
- local: robocerebra
title: RoboCerebra
- local: robomme
title: RoboMME
- local: envhub_isaaclab_arena
title: NVIDIA IsaacLab Arena Environments
title: "Benchmarks"
- sections:
- local: introduction_processors
title: Introduction to Robot Processors
- local: debug_processor_pipeline
title: Debug your processor pipeline
- local: implement_your_own_processor
title: Implement your own processor
- local: processors_robots_teleop
title: Processors for Robots and Teleoperators
- local: env_processor
title: Environment Processors
- local: action_representations
title: Action Representations
title: "Robot Processors"
- sections:
- local: so101
title: SO-101
- local: so100
title: SO-100
- local: koch
title: Koch v1.1
- local: lekiwi
title: LeKiwi
- local: hope_jr
title: Hope Jr
- local: reachy2
title: Reachy 2
- local: unitree_g1
title: Unitree G1
- local: earthrover_mini_plus
title: Earth Rover Mini
- local: omx
title: OMX
- local: openarm
title: OpenArm
title: "Robots"
- sections:
- local: phone_teleop
title: Phone
title: "Teleoperators"
- sections:
- local: cameras
title: Cameras
title: "Sensors"
- sections:
- local: torch_accelerators
title: PyTorch accelerators
title: "Supported Hardware"
- sections:
- local: notebooks
title: Notebooks
- local: feetech
title: Updating Feetech Firmware
- local: damiao
title: Damiao Motors and CAN Bus
title: "Resources"
- sections:
- local: contributing
title: Contribute to LeRobot
- local: backwardcomp
title: Backward compatibility
title: "About"