Datasets:
File size: 2,069 Bytes
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license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
configs:
- config_name: default
data_files: data/*/*.parquet
---
This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
# Dataset Card for **Smol-LIBERO**
## Dataset Summary
Smol-LIBERO is a **compact version of the LIBERO benchmark**, built to make experimentation fast and accessible.
At just **1.79 GB** (compared to ~34 GB for the full LIBERO), it contains fewer trajectories and cameras while keeping the same multimodal structure.
Each sample includes:
- **Images** from two fixed cameras
- **Two types of robot state** (end-effector pose + gripper, and full 7-DoF joint positions)
- **Actions** (7-DoF joint commands)
This setup is especially useful for comparing **low-dimensional state inputs** with **high-dimensional visual inputs**, or combining them in multimodal training.
---
## Dataset Structure
### Data Fields
- **`observation.images.image`**: 256×256×3 RGB image (camera 1)
- **`observation.images.image2`**: 256×256×3 RGB image (camera 2)
- **`observation.state`** *(8 floats)*: end-effector Cartesian pose + gripper
`[x, y, z, roll, pitch, yaw, gripper, gripper]`
- **`observation.state.joint`** *(7 floats)*: full joint angles
`[joint_1, …, joint_7]`
- **`action`** *(7 floats)*: target joint commands
---
## Why is it smaller than LIBERO?
- **Fewer trajectories/tasks** → subset of the full benchmark
- **Only two camera views** → reduced visual redundancy
- **Reduced total frames** → shorter episodes or lower FPS
That’s why Smol-LIBERO is **1.79 GB instead of 34 GB**.
---
## Intended Uses
- Quick prototyping and debugging
- Comparing joint-space vs. Cartesian state inputs
- Training small VLA baselines before scaling to LIBERO
---
## Limitations
- Smaller task and visual diversity compared to LIBERO
- Only two fixed camera views
- May not fully represent generalization behavior on larger benchmarks
## Citation
**BibTeX:**
```bibtex
[More Information Needed]
``` |