stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation
Paper • 2605.21800 • Published
Datasets:
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episode_idx int32 0 2.92k | step_idx int32 0 200 | action list | ep_idx list | finger_pos list | id list | observation list | pixels imagewidth (px) 224 224 | qpos list | qvel list | render_time list | reward list | score list | success list | target_pos list | terminated list | truncated list |
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End of preview. Expand in Data Studio
LeWM Reacher
DeepMind Control Reacher trajectories from quentinll/lewm-reacher, repackaged for direct inspection and streaming.
Open in the LeWM Dataset Visualizer
Data
- 10,000 episodes
- 2,010,000 timesteps
- 201 steps per episode
- 224 × 224 RGB observations
Core fields are pixels (RGB), action (float32[2]), and observation (float32[6]). The source diagnostics are retained: finger_pos, qpos, qvel, target_pos, reward, score, success, terminated, truncated, render_time, id, and ep_idx. episode_idx and step_idx are canonical indices.
train.lance is the indexed training table. viewer/ is a Parquet mirror for the Hugging Face dataset viewer and the LeWM visualizer.
from stable_worldmodel.data.formats.lance import LanceDataset
dataset = LanceDataset(
"hf://datasets/fracapuano/lewm-reacher/train.lance",
num_steps=16,
)
sample = dataset[0]
Provenance
- Source:
quentinll/lewm-reacher - Source revision:
e70a080d0d04c6072123c9ebd343acf7fff28dbf - Source archive SHA-256:
4ff2385e49712caa89f21b8e0a246e2614b621d3f22cf2d1224d845e879a1cc2 - Lance schema implementation:
stable-worldmodelrevision85d325cc77449c3889e52b03ee7e561cb2fc0383 - License: MIT
All episode boundaries and numeric fields were validated against the source after canonical float32 normalization. Across 145 sampled frames, JPEG pixel MAE averaged 0.903/255 with a maximum sample mean of 0.925/255. Another 130 rows verified the Parquet mirror against Lance, including identical JPEG bytes.
The stable-worldmodel paper reports faster local and remote trajectory loading with Lance than HDF5 on PushT.
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