README.md

metadata

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: 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

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

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:

@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:

@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, alongside the policy and world-model training code and the calibration study itself.

DreamGrasp is created and maintained by Zaid Ghazal.