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0d80452 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | # LIBERO-plus
LIBERO-plus is a **robustness benchmark** for Vision-Language-Action (VLA) models built on top of [LIBERO](./libero). It systematically stress-tests policies by applying **seven independent perturbation dimensions** to the original LIBERO task set, exposing failure modes that standard benchmarks miss.
- Paper: [In-depth Robustness Analysis of Vision-Language-Action Models](https://arxiv.org/abs/2510.13626)
- GitHub: [sylvestf/LIBERO-plus](https://github.com/sylvestf/LIBERO-plus)
- Dataset: [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
## Perturbation dimensions
LIBERO-plus creates ~10 000 task variants by perturbing each original LIBERO task along these axes:
| Dimension | What changes |
| --------------------- | ----------------------------------------------------- |
| Objects layout | Target position, presence of confounding objects |
| Camera viewpoints | Camera position, orientation, field-of-view |
| Robot initial states | Manipulator start pose |
| Language instructions | LLM-rewritten task description (paraphrase / synonym) |
| Light conditions | Intensity, direction, color, shadow |
| Background textures | Scene surface and object appearance |
| Sensor noise | Photometric distortions and image degradation |
## Available task suites
LIBERO-plus covers the same five suites as LIBERO:
| Suite | CLI name | Tasks | Max steps | Description |
| -------------- | ---------------- | ----- | --------- | -------------------------------------------------- |
| LIBERO-Spatial | `libero_spatial` | 10 | 280 | Tasks requiring reasoning about spatial relations |
| LIBERO-Object | `libero_object` | 10 | 280 | Tasks centered on manipulating different objects |
| LIBERO-Goal | `libero_goal` | 10 | 300 | Goal-conditioned tasks with changing targets |
| LIBERO-90 | `libero_90` | 90 | 400 | Short-horizon tasks from the LIBERO-100 collection |
| LIBERO-Long | `libero_10` | 10 | 520 | Long-horizon tasks from the LIBERO-100 collection |
<Tip warning={true}>
Installing LIBERO-plus **replaces** vanilla LIBERO — it uninstalls `hf-libero`
so that `import libero` resolves to the LIBERO-plus fork. You cannot have both
installed at the same time. To switch back to vanilla LIBERO, uninstall the
fork and reinstall with `pip install -e ".[libero]"`.
</Tip>
## Installation
### System dependencies (Linux only)
```bash
sudo apt install libexpat1 libfontconfig1-dev libmagickwand-dev
```
### Python package
```bash
pip install -e ".[libero]" "robosuite==1.4.1" bddl easydict mujoco wand scikit-image gym
git clone https://github.com/sylvestf/LIBERO-plus.git
cd LIBERO-plus && pip install --no-deps -e .
pip uninstall -y hf-libero # so `import libero` resolves to the fork
```
LIBERO-plus is installed from its GitHub fork rather than a pyproject extra — the fork ships as a namespace package that pip can't handle, so it must be cloned and added to `PYTHONPATH`. See `docker/Dockerfile.benchmark.libero_plus` for the canonical install. MuJoCo is required, so only Linux is supported.
<Tip>
Set the MuJoCo rendering backend before running evaluation:
```bash
export MUJOCO_GL=egl # headless / HPC / cloud
```
</Tip>
### Download LIBERO-plus assets
LIBERO-plus ships its extended asset pack separately. Download `assets.zip` from the [Hugging Face dataset](https://huggingface.co/datasets/Sylvest/LIBERO-plus/tree/main) and extract it into the LIBERO-plus package directory:
```bash
# After installing the package, find where it was installed:
python -c "import libero; print(libero.__file__)"
# Then extract assets.zip into <package_root>/libero/assets/
```
## Evaluation
### Default evaluation (recommended)
Evaluate across the four standard suites (10 episodes per task):
```bash
lerobot-eval \
--policy.path="your-policy-id" \
--env.type=libero_plus \
--env.task=libero_spatial,libero_object,libero_goal,libero_10 \
--eval.batch_size=1 \
--eval.n_episodes=10 \
--env.max_parallel_tasks=1
```
### Single-suite evaluation
Evaluate on one LIBERO-plus suite:
```bash
lerobot-eval \
--policy.path="your-policy-id" \
--env.type=libero_plus \
--env.task=libero_spatial \
--eval.batch_size=1 \
--eval.n_episodes=10
```
- `--env.task` picks the suite (`libero_spatial`, `libero_object`, etc.).
- `--env.task_ids` restricts to specific task indices (`[0]`, `[1,2,3]`, etc.). Omit to run all tasks in the suite.
- `--eval.batch_size` controls how many environments run in parallel.
- `--eval.n_episodes` sets how many episodes to run per task.
### Multi-suite evaluation
Benchmark a policy across multiple suites at once by passing a comma-separated list:
```bash
lerobot-eval \
--policy.path="your-policy-id" \
--env.type=libero_plus \
--env.task=libero_spatial,libero_object \
--eval.batch_size=1 \
--eval.n_episodes=10
```
### Control mode
LIBERO-plus supports two control modes — `relative` (default) and `absolute`. Different VLA checkpoints are trained with different action parameterizations, so make sure the mode matches your policy:
```bash
--env.control_mode=relative # or "absolute"
```
### Policy inputs and outputs
**Observations:**
- `observation.state` — 8-dim proprioceptive features (eef position, axis-angle orientation, gripper qpos)
- `observation.images.image` — main camera view (`agentview_image`), HWC uint8
- `observation.images.image2` — wrist camera view (`robot0_eye_in_hand_image`), HWC uint8
**Actions:**
- Continuous control in `Box(-1, 1, shape=(7,))` — 6D end-effector delta + 1D gripper
### Recommended evaluation episodes
For reproducible benchmarking, use **10 episodes per task** across all four standard suites (Spatial, Object, Goal, Long). This gives 400 total episodes and matches the protocol used for published results.
## Training
### Dataset
A LeRobot-format training dataset for LIBERO-plus is available at:
- [lerobot/libero_plus](https://huggingface.co/datasets/lerobot/libero_plus)
### Example training command
```bash
lerobot-train \
--policy.type=smolvla \
--policy.repo_id=${HF_USER}/smolvla_libero_plus \
--policy.load_vlm_weights=true \
--dataset.repo_id=lerobot/libero_plus \
--env.type=libero_plus \
--env.task=libero_spatial \
--output_dir=./outputs/ \
--steps=100000 \
--batch_size=4 \
--eval.batch_size=1 \
--eval.n_episodes=1 \
--env_eval_freq=1000
```
## Relationship to LIBERO
LIBERO-plus is a drop-in extension of LIBERO:
- Same Python gym interface (`LiberoEnv`, `LiberoProcessorStep`)
- Same camera names and observation/action format
- Same task suite names
- Installs under the same `libero` Python package name (different GitHub repo)
To use the original LIBERO benchmark, see [LIBERO](./libero) and use `--env.type=libero`.
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