Datasets:

JU9999
/

lewm_pusht_64px

ArXiv:

Tags:

world-models

offline-rl

robotics

License:

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Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/h5_columns/action/[]) changed from array to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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LeWorldModel Push-T (Diffusion Policy demos x noisy replays), 64 px

This is a redistribution of third-party data. We claim no ownership and make no endorsement claims; all credit to the original authors.

Upstream sources and licenses

LeWorldModel (Maes, Le Lidec et al. 2026), https://huggingface.co/datasets/quentinll/lewm-pusht (pusht_expert_train.h5.zst), MIT, Copyright (c) 2026 Lucas Maes.
Human demonstrations: Diffusion Policy (Chi et al. 2023), https://github.com/real-stanford/diffusion_policy, MIT, Copyright (c) 2023 Columbia Artificial Intelligence and Robotics Lab; replayed with noise as in DINO-WM (https://github.com/gaoyuezhou/dino_wm, MIT, Copyright (c) 2025 gaoyuezhou).

Source revision: quentinll/lewm-pusht @ 655cd446b9929369d7d406001da85c15d1457850, file pusht_expert_train.h5.zst. The full license texts are in LICENSE.

Modifications

Frames resized 224 -> 64 px with antialiased bilinear filtering (torch F.interpolate, antialias=True), stored uint8 channels-first.
Unpadded flat layout: converted from the flat HDF5 (ep_len / ep_offset) to a TensorDict memmap whose tensordict/frames holds every episode's rows end to end (no padding rows, no mask) and whose tensordict/episodes holds ep_offset / ep_len (episode e, step t is frames[ep_offset[e] + t]) and the per-episode columns.
Actions unchanged, except the NaN action the source stores on each episode's last row, which is stored as 0.0.
No next/pixels / next/state.
State = the 7-d state column; proprio (a subset of it) dropped. tensordict/episodes adds source_demo_id (the Diffusion-Policy / lerobot/pusht episode each replay comes from, matched exactly on the frame-0 agent position) and clean_replay.

TensorDict memmap (tensordict/) readable with tensordict.TensorDict.load_memmap, plus data/metadata.json (full provenance of the conversion) and tensordict/episodes. Converted with the BiLip-JEPA code base (src/bilip_jepa/envs/lewm/collect_data.py); load there with bilip_jepa.dataset_utils.resolve_dataset_ref({'kind': 'hf', 'repo_id': 'JU9999/lewm_pusht_64px', 'revision': 'v0'}).

layout: "flat"
num_trajectories: 18685
num_steps: 246
num_rows: 2336736
image_shape: [3, 64, 64]
raw_transition_count: 2336736
stored_rows: 2336736
dt: 0.1
action_repeat: 1
action_low: [-1.0, -1.0]
action_high: [1.0, 1.0]
action_min: [-1.4947075843811035, -1.4655311107635498]
action_max: [2.0426697731018066, 1.7920565605163574]
state_columns: ["state[0]", "state[1]", "state[2]", "state[3]", "state[4]", "state[5]", "state[6]"]
trajectory_lengths_min_max_mean: [49, 246, 125.05945945945946]

Citation

Please cite the original works:

@article{maes_lelidec2026lewm,
  title={LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels},
  author={Maes, Lucas and Le Lidec, Quentin and Scieur, Damien and LeCun, Yann and Balestriero, Randall},
  journal={arXiv preprint arXiv:2603.19312},
  year={2026}
}

@inproceedings{chi2023diffusionpolicy,
  title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
  author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
  booktitle={Robotics: Science and Systems},
  year={2023}
}

@article{zhou2024dinowm,
  title={DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning},
  author={Zhou, Gaoyue and Pan, Hengkai and LeCun, Yann and Pinto, Lerrel},
  journal={arXiv preprint arXiv:2411.04983},
  year={2024}
}

File SHA-256 (as converted, before upload)

3bada3639e30e43127a061e76b43cf6a5a0d4af576f8a8841bfb5601e34bc9a8  data/metadata.json
969c0d9b14c0bfe71e15b5aaadcfce24bcf9a6715cc0cc4aeb03862563b2ecbd  tensordict/episodes/clean_replay.memmap
b3ae3f91caaff47ed4066e80738f01ccb849b49855187b253360cd076daa8ef4  tensordict/episodes/ep_len.memmap
786c0dbea2cd84ad557372d3c7c19d217246c7cee3df25f9a140c803fe9b4ec7  tensordict/episodes/ep_offset.memmap
91dd53c36257f4bbed447b1746fbd9125b9bc1dec3316a8674caa1f4dd5abf11  tensordict/episodes/meta.json
a6e81b9e2457f7d887b61fc28bd45e8e06b317b1093ad98cb9c9d3dc9145acfe  tensordict/episodes/source_demo_id.memmap
4f287a471d1763067b10351f1d67cd7e87744cfbc731b4f6e62c98e388e13da6  tensordict/episodes/source_episode.memmap
db62c4b5317b53e1e256140146b512419a79f27f805f411637a18be8bcc18011  tensordict/frames/action.memmap
0f824a340baeac8123171bfc05e64fd8b9cd78dba05b05c4ca9c74290723b098  tensordict/frames/done.memmap
0fb31855560a02b6059547661265dfa8545dac6a14eb9157506f96478a552c5a  tensordict/frames/is_init.memmap
b7a9dd6ad0308e3a15a9d2a06db18371bc521d92d48783fd82789749c22f2697  tensordict/frames/meta.json
0f824a340baeac8123171bfc05e64fd8b9cd78dba05b05c4ca9c74290723b098  tensordict/frames/next/done.memmap
a6e71b8dd967adc34514c43d8753074c19e941c89795cc0e014dcb181611af3b  tensordict/frames/next/meta.json
0f824a340baeac8123171bfc05e64fd8b9cd78dba05b05c4ca9c74290723b098  tensordict/frames/next/terminated.memmap
63c9ef9c7a47f072fdae6a072639c1f7fa9a5902a994a32048ccd97f7e2d125d  tensordict/frames/next/truncated.memmap
139e94d412625a51e92640314bd19f89a807896e907711b69bc4c0aed1fceee8  tensordict/frames/pixels.memmap
401d4d8c396bd9d06821be4739373e02c7617334939a487a9229a0f316be85a8  tensordict/frames/state/meta.json
8a111cc5a53925c99a0b9ca813eb932a47664fa2c0c7309e7b84935afa3f2047  tensordict/frames/state/observation.memmap
0f824a340baeac8123171bfc05e64fd8b9cd78dba05b05c4ca9c74290723b098  tensordict/frames/terminated.memmap
15c147179d71941bd20ae5ab8ee7a1b8b094f1928c0abc591dbd88bce26714da  tensordict/frames/time.memmap
4cc07d5846959e6864d4a2519fb01653757b35f8c6c4bc6eb452a0a433d0b7c6  tensordict/frames/traj_ids.memmap
63c9ef9c7a47f072fdae6a072639c1f7fa9a5902a994a32048ccd97f7e2d125d  tensordict/frames/truncated.memmap
e1632101d670e417b829d3093b7b11a3b869c4870f47af7cedfc6f22ab9eeabf  tensordict/meta.json

Normalization statistics (`stats/normalization.json`)

Whole-dataset statistics: every split, real rows only, computed by scripts/compute_dataset_stats.py (module bilip_jepa.dataset_stats) of the ActionInjection_JEPA code. Training, the data loader, the planners and the evaluation build their scalers from this file.

Data read: revision dc1f324f06d15c3108217e07c5d9779c136c6751 (tag v0), layout flat; 18,685 episodes, 2,336,736 real rows (0 padding rows excluded), 2,318,051 action rows.
Rows: states, pixels and references over rows t = 0 .. T_e - 1 of every episode e; actions over the transitions t = 0 .. T_e - 2 (the last row's action has no successor and is never read).
Moments per coordinate j over those N rows, float64: mean_j = (1/N) sum_r x_rj, std_j = max(sqrt((1/N) sum_r (x_rj - mean_j)^2), 1e-6) (biased), min_j and max_j.
Action percentiles p_low_j / p_high_j = the 0.1 % / 99.9 % percentiles of coordinate j over the action rows: sort x_(0) <= .. <= x_(N-1), h = (p/100)(N - 1), k = floor(h), q_p = x_(k) + (h - k)(x_(k+1) - x_(k)) (linear interpolation, numpy's default).
Model units (z-score): x'_j = (x_j - mean_j) / std_j, inverse x_j = x'_j std_j + mean_j. Each scaler dict carries a units marker {format: bilip_jepa.affine_scaler, version: 1, kind, mode}.
Planner bounds (raw; since stats file v2, 2026-09-27): low_j = max(p_low_j, env_low_j) and high_j = min(p_high_j, env_high_j) on coordinates where the environment enforces its declared limits, else low_j = p_low_j, high_j = p_high_j (v1 used min_j / max_j). In model units: [(low_j - mean_j) / std_j, (high_j - mean_j) / std_j]. Coverage: the fraction of action rows with every coordinate inside [low_j, high_j], and the box-area ratio prod_j (high_j - low_j) / (max_j - min_j).

Actions (2: dx_target_rel, dy_target_rel):

mean [-0.007922, 0.006912], std [0.2091, 0.2074]
data min [-1.495, -1.466], data max [2.043, 1.792]
0.1 % percentile [-0.8041, -0.7388], 99.9 % percentile [0.8948, 0.7789]
environment's declared limits [[-1, -1], [1, 1]], enforced by the environment: [false, false] (stable_worldmodel PushT-v1 declares Box(-1, 1, (2,)); the env does not clip)
action rows outside the declared limits: 2,904 (0.1253 %)
planner bounds (raw) [[-0.8041, -0.7388], [0.8948, 0.7789]]; in model units [[-3.807, -3.595], [4.316, 3.722]]
action rows inside the planner bounds: 2,308,893 of 2,318,051 (99.6 %); box-area ratio (planner box / data-range box) 0.2238
no-actuation command (raw) [0, 0] = [0.03788, -0.03332] in model units, i.e. (raw - mean) / std (relative target 0: agent target = agent position (stable_worldmodel PushT))

State state/observation (7 raw columns: state[0], state[1], state[2], state[3], state[4], state[5], state[6]):

angle columns [4] (state[4]) are mapped to (cos, sin) before the moments; 8 features: state[0], state[1], state[2], state[3], cos(state[4]), sin(state[4]), state[5], state[6]
feature mean [228.8, 292.2, 243.6, 275.3, 0.3931, 0.3245, -2.921, 2.549], std [103.4, 98.93, 71.62, 73.38, 0.5982, 0.6184, 77.5, 76.86], min [-153, -90.6, -32.84, -1.858, -1, -1, -558.1, -546], max [666.1, 723.6, 507.6, 538.8, 1, 1, 762.6, 669.2]
raw column min [-153, -90.6, -32.84, -1.858, 1.376e-06, -558.1, -546], max [666.1, 723.6, 507.6, 538.8, 6.283, 762.6, 669.2]

Pixels: per channel over all real frames of x / 255: mean [0.9685, 0.9776, 0.9741], std [0.09591, 0.06915, 0.0756].