---
license: mit
task_categories:
- robotics
tags:
- lerobot
- libero
- robotics
- robot-learning
- world-model-evaluation
- imitation-learning
- vision-language-action
- policy-evaluation
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/chunk-*/file-*.parquet
---
![DreamGrasp banner](assets/dreamgrasp-hf-banner.png)
# DreamGrasp: Processed LIBERO Manipulation Demonstrations

**Does a robot policy's evaluation still mean something if it never touched a real simulator, only a world model's imagination of one?**

This dataset is the shared training data behind that question, a single, ready-to-train release built from LIBERO's manipulation demonstrations (`libero_spatial`, `libero_object`, `libero_goal`). It provides:

- **Fixed, versioned train / validation / test / held-out splits**, so every result trained on this data is directly reproducible and comparable across runs
- **Pre-computed action and proprioceptive normalization statistics**, shared by every model in the project rather than recomputed per-run
- **Synchronized agentview and wrist-camera video** for every episode, converted to LeRobotDataset v3 and ready to drop into LeRobot's data loaders
- **The exact data used to train both the policy and the five-tier world-model family** in the DreamGrasp calibration study, start here instead of re-deriving splits and stats from scratch

**Project links:** [GitHub repository](https://github.com/ZaidGhazal/world-models-eval)

## Contents

- 1,500 episodes
- 200,485 frames
- 30 tasks across 3 LIBERO suites
- 20 FPS
- 128 × 128 RGB `agentview` and wrist videos (native LIBERO resolution, not upscaled)
- Panda proprioceptive state, 8 dimensions: end-effector position, axis-angle orientation, gripper state
- Normalized 7D delta end-effector action plus gripper command

## Quickstart

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset("ZaidGhazal/world-models-eval")

episode = dataset[0]
print(episode["observation.images.agentview"].shape)  # (T, 3, 128, 128)
print(episode["action"].shape)                          # (T, 7)
```

Normalization statistics and the frozen split assignment used throughout the DreamGrasp project are versioned in the repo under `configs/norm_stats.json` and `configs/splits.json` — load these rather than recomputing your own if you want directly comparable results.

## Features

| Field                          | Description                                                             |
| ------------------------------ | ------------------------------------------------------------------------ |
| `observation.images.agentview` | 128 × 128 RGB video                                                     |
| `observation.images.wrist`     | 128 × 128 RGB video                                                     |
| `observation.state`            | float32 `(8,)` proprioceptive state                                     |
| `action`                       | float32 `(7,)` action, normalized to `[-1, 1]` using train episodes only |
| `task_index`                   | Integer task id, resolved via `meta/tasks.parquet`                      |


## What you can build with DreamGrasp

- **Train and evaluate manipulation policies** on a clean, ready-to-use LIBERO benchmark, with splits and normalization already handled
- **Develop and test world models for robotics**, using real demonstration data and a fixed held-out set for fair comparisons
- **Research how well simulated or imagined rollouts predict real policy performance**, the question behind DreamGrasp and related work
- **Study generalization and distribution shift in imitation learning**, using tasks intentionally withheld from training

## Splits and Normalization

The LeRobot metadata ships everything as a single `train` split; DreamGrasp uses the frozen episode assignment in `configs/splits.json`:

- 960 train episodes
- 120 validation episodes
- 120 test episodes
- 300 held-out episodes, from tasks excluded entirely from training — used specifically to test evaluation reliability under distribution shift

Action and proprioceptive normalization statistics are stored in `configs/norm_stats.json`. Use these exact stats if you want results directly comparable to the DreamGrasp project's own policy and world-model training.

## Limitations

- **Simulation-only.** No real-robot trajectories; sim-to-real transfer isn't evaluated. In exchange, every result is fully reproducible without hardware.
- **Single embodiment.** Franka Emika Panda only, so results may not transfer to other morphologies — but embodiment never confounds a comparison.
- **Fixed task scope.** 30 tasks across LIBERO's spatial, object, and goal suites; narrow by design so everything runs end to end on a single GPU.
- **Train-split normalization stats.** Held-out tasks may see out-of-range action values. Intentional: held-out evaluation stays a genuine test of generalization.

## Source and Citation

If you use this dataset, please cite it:

```bibtex
@misc{ghazal2026dreamgrasp,
  title={DreamGrasp: Processed LIBERO Manipulation Demonstrations for World-Model Evaluation},
  author={Ghazal, Zaid},
  year={2026},
  howpublished={\url{https://huggingface.co/datasets/ZaidGhazal/world-models-eval}}
}
```

This dataset is converted from the original LIBERO demonstration HDF5 files. If you use it, please also cite the source benchmark:

```bibtex
@article{liu2023libero,
  title={LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
  author={Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and Liu, Qiang and Zhu, Yuke and Stone, Peter},
  journal={arXiv preprint arXiv:2306.03310},
  year={2023}
}
```

The processing pipeline that produced this dataset from the raw LIBERO files is open-sourced in the DreamGrasp [GitHub repository](https://github.com/ZaidGhazal/world-models-eval), alongside the policy and world-model training code and the calibration study itself.
---

DreamGrasp is created and maintained by **[Zaid Ghazal](https://github.com/ZaidGhazal)**.