Hugging Face's logo

Therat08
/
Training2

Model card Files
xet
 Community
Training2
File size: 8,240 Bytes
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
# VLABench

[VLABench](https://github.com/OpenMOSS/VLABench) is a large-scale benchmark for **language-conditioned robotic manipulation with long-horizon reasoning**. The upstream suite covers 100 task categories across 2,000+ objects and evaluates six dimensions of robot intelligence: mesh & texture understanding, spatial reasoning, world-knowledge transfer, semantic instruction comprehension, physical-law understanding, and long-horizon planning. Built on MuJoCo / dm_control with a Franka Panda 7-DOF arm. LeRobot exposes **43 of these tasks** through `--env.task` (21 primitives + 22 composites, see [Available tasks](#available-tasks) below).

- Paper: [VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning](https://arxiv.org/abs/2412.18194)
- GitHub: [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench)
- Project website: [vlabench.github.io](https://vlabench.github.io)
- Pretrained policy: [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench)

<img
  src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/lerobot/vlabench.png"
  alt="VLABench benchmark overview"
  width="85%"
/>

## Available tasks

VLABench ships two task suites covering **43 task categories** in LeRobot's `--env.task` surface:

| Suite     | CLI name    | Tasks | Description                                                      |
| --------- | ----------- | ----- | ---------------------------------------------------------------- |
| Primitive | `primitive` | 21    | Single / few-skill combinations (select, insert, physics QA)     |
| Composite | `composite` | 22    | Multi-step reasoning and long-horizon planning (cook, rearrange) |

**Primitive tasks:** `select_fruit`, `select_toy`, `select_chemistry_tube`, `add_condiment`, `select_book`, `select_painting`, `select_drink`, `insert_flower`, `select_billiards`, `select_ingredient`, `select_mahjong`, `select_poker`, and physical-reasoning tasks (`density_qa`, `friction_qa`, `magnetism_qa`, `reflection_qa`, `simple_cuestick_usage`, `simple_seesaw_usage`, `sound_speed_qa`, `thermal_expansion_qa`, `weight_qa`).

**Composite tasks:** `cluster_billiards`, `cluster_book`, `cluster_drink`, `cluster_toy`, `cook_dishes`, `cool_drink`, `find_unseen_object`, `get_coffee`, `hammer_nail`, `heat_food`, `make_juice`, `play_mahjong`, `play_math_game`, `play_poker`, `play_snooker`, `rearrange_book`, `rearrange_chemistry_tube`, `set_dining_table`, `set_study_table`, `store_food`, `take_chemistry_experiment`, `use_seesaw_complex`.

`--env.task` accepts three forms:

- a single task name (`select_fruit`)
- a comma-separated list (`select_fruit,heat_food`)
- a suite shortcut (`primitive`, `composite`, or `primitive,composite`)

## Installation

VLABench is **not on PyPI** — its only distribution is the [OpenMOSS/VLABench](https://github.com/OpenMOSS/VLABench) GitHub repo — so LeRobot does not expose a `vlabench` extra. Install it manually as an editable clone, alongside the MuJoCo / dm_control pins VLABench needs, then fetch the mesh assets:

```bash
# After following the standard LeRobot installation instructions.

git clone https://github.com/OpenMOSS/VLABench.git ~/VLABench
git clone https://github.com/motion-planning/rrt-algorithms.git ~/rrt-algorithms
pip install -e ~/VLABench -e ~/rrt-algorithms
pip install "mujoco==3.2.2" "dm-control==1.0.22" \
            open3d colorlog scikit-learn openai gdown

python ~/VLABench/scripts/download_assets.py
```

<Tip>
VLABench requires Linux (`sys_platform == 'linux'`) and Python 3.10+. Set the MuJoCo rendering backend before running:

```bash
export MUJOCO_GL=egl  # for headless servers (HPC, cloud)
```

</Tip>

## Evaluation

All eval snippets below mirror the command CI runs (see `.github/workflows/benchmark_tests.yml`). The `--rename_map` argument maps VLABench's `image` / `second_image` / `wrist_image` camera keys onto the three-camera (`camera1` / `camera2` / `camera3`) input layout the released `smolvla_vlabench` policy was trained on.

### Single-task evaluation (recommended for quick iteration)

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_vlabench \
  --env.type=vlabench \
  --env.task=select_fruit \
  --eval.batch_size=1 \
  --eval.n_episodes=10 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
```

### Multi-task evaluation

Pass a comma-separated list of tasks:

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_vlabench \
  --env.type=vlabench \
  --env.task=select_fruit,select_toy,add_condiment,heat_food \
  --eval.batch_size=1 \
  --eval.n_episodes=10 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
```

### Suite-wide evaluation

Run an entire suite (all 21 primitives or all 22 composites):

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_vlabench \
  --env.type=vlabench \
  --env.task=primitive \
  --eval.batch_size=1 \
  --eval.n_episodes=10 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  --env.max_parallel_tasks=1 \
  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
```

Or both suites:

```bash
lerobot-eval \
  --policy.path=lerobot/smolvla_vlabench \
  --env.type=vlabench \
  --env.task=primitive,composite \
  --eval.batch_size=1 \
  --eval.n_episodes=10 \
  --eval.use_async_envs=false \
  --policy.device=cuda \
  --env.max_parallel_tasks=1 \
  '--rename_map={"observation.images.image": "observation.images.camera1", "observation.images.second_image": "observation.images.camera2", "observation.images.wrist_image": "observation.images.camera3"}'
```

### Recommended evaluation episodes

**10 episodes per task** for reproducible benchmarking (210 total for the full primitive suite, 220 for composite). Matches the protocol in the VLABench paper.

## Policy inputs and outputs

**Observations:**

- `observation.state` — 7-dim end-effector state (position xyz + Euler xyz + gripper)
- `observation.images.image` — front camera, 480×480 HWC uint8
- `observation.images.second_image` — second camera, 480×480 HWC uint8
- `observation.images.wrist_image` — wrist camera, 480×480 HWC uint8

**Actions:**

- Continuous control in `Box(-1, 1, shape=(7,))` — 3D position + 3D Euler orientation + 1D gripper.

## Training

### Datasets

Pre-collected VLABench datasets in LeRobot format on the Hub:

- [`VLABench/vlabench_primitive_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_primitive_ft_lerobot_video) — 5,000 episodes, 128 tasks, 480×480 images.
- [`VLABench/vlabench_composite_ft_lerobot_video`](https://huggingface.co/datasets/VLABench/vlabench_composite_ft_lerobot_video) — 5,977 episodes, 167 tasks, 224×224 images.

### Example training command

Fine-tune a SmolVLA base on the primitive suite:

```bash
lerobot-train \
  --policy.type=smolvla \
  --policy.repo_id=${HF_USER}/smolvla_vlabench_primitive \
  --policy.load_vlm_weights=true \
  --policy.push_to_hub=true \
  --dataset.repo_id=VLABench/vlabench_primitive_ft_lerobot_video \
  --env.type=vlabench \
  --env.task=select_fruit \
  --output_dir=./outputs/smolvla_vlabench_primitive \
  --steps=100000 \
  --batch_size=4 \
  --env_eval_freq=5000 \
  --eval.batch_size=1 \
  --eval.n_episodes=1 \
  --save_freq=10000
```

## Reproducing published results

The released checkpoint [`lerobot/smolvla_vlabench`](https://huggingface.co/lerobot/smolvla_vlabench) was trained on the primitive-suite dataset above and is evaluated with the [Single-task](#single-task-evaluation-recommended-for-quick-iteration) / [Suite-wide](#suite-wide-evaluation) commands. CI runs a 10-primitive-task smoke eval (one episode each) on every PR touching the benchmark.