Semi-Supervised Cross-Domain Imitation Learning
Paper • 2602.10793 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Illegal slicing argument for scalar dataspace
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 83, in _generate_tables
pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 267, in _recursive_load_arrays
arr = _recursive_load_arrays(obj, features[path], start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 269, in _recursive_load_arrays
arr = _load_array(obj, path, start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 236, in _load_array
arr = dset[start:end]
~~~~^^^^^^^^^^^
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/dataset.py", line 931, in __getitem__
selection = sel2.select_read(fspace, args)
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 101, in select_read
return ScalarReadSelection(fspace, args)
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 86, in __init__
raise ValueError("Illegal slicing argument for scalar dataspace")
ValueError: Illegal slicing argument for scalar dataspaceNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CDIL datasets
Source- and target-domain datasets for Semi-Supervised Cross-Domain Imitation Learning (AdaptDICE). Paper: arXiv:2602.10793. Code: NYCU-RL-Bandits-Lab/CDIL.
Usage
The training script reads these files from ./dataset (--dataset_dir) by default. From the root of the code repo, run:
hf download rl-bandit-lab/CDIL --repo-type dataset --local-dir dataset
Then pass the filenames to train_il.py with --dataset_file_names="[<target expert 5>, <target expert 400>, <target random>]". See scripts/run_train.sh.
Files
| Benchmark | Domain | Files |
|---|---|---|
| MuJoCo | Source (Hopper-v2, HalfCheetah-v2, Ant-v2) | {hopper,halfcheetah,ant}_{expert,random}-v2.hdf5 (unmodified D4RL, CC BY 4.0) |
| MuJoCo | Target (modified XMLs in env/) |
target_{hopper,cheetah,ant}_{5,400}_*.npz (expert), target_{Hopper,HalfCheetah,Ant}-v3_random_{50,100,500}.npz |
| Robosuite | Source (Panda) | {Lift,Door,Wipe}_Panda_400_*.npz (expert), {Lift,Door,Wipe}_Panda_random_1600.npz |
| Robosuite | Target (UR5e) | {Lift,Door,Wipe}_UR5e_{5,400}_*.npz (expert), {Lift,Door,Wipe}_UR5e_random_{50,100,500}.npz |
Expert files are named <env>_<num_trajs>_<avg_return>_<avg_episode_len>.npz. The 5-trajectory file supplies the labeled target expert demos. The 400-trajectory file supplies the expert part of the unlabeled imperfect data. The random_N file supplies the random part.
Each .npz contains init_obs, obs, action, next_obs, and done, with transitions stored trajectory after trajectory. Robosuite uses the JOINT_VELOCITY controller, object-state + robot0_proprio-state observations, reward_shaping=True, and horizon=500.
| Env | Source state/action | Target state/action |
|---|---|---|
| Hopper | 11 / 3 | 13 / 4 |
| HalfCheetah | 17 / 6 | 23 / 9 |
| Ant | 27 / 8 | 31 / 10 |
| Lift | 42 / 8 | 47 / 7 |
| Door | 46 / 8 | 51 / 7 |
| Wipe | 37 / 7 | 34 / 6 |
How the data was generated
- Expert data (everything except D4RL) was collected by rolling out SAC policies trained to expert level in each domain.
- Random data was collected by uniform-random rollouts. See
utils.sample_demonstrationsin the code repo (it collects fresh random rollouts whenload_path=None).
Citation
@misc{chu2026semi,
title = {Semi-Supervised Cross-Domain Imitation Learning},
author = {Chu, Li-Min and Ma, Kai-Siang and Chen, Ming-Hong and Hsieh, Ping-Chun},
year = {2026},
eprint = {2602.10793},
archivePrefix = {arXiv}
}
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