Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation
Paper • 2603.15759 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
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/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/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
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 71, 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.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
UR10e Linear Gripper — Jig / Bottom Enclosure — SimDist Stage-2 Dataset
Action-conditioned simulation rollouts for pretraining a latent world model, generated with the Simulation Distillation (SimDist) stage-2 procedure (arXiv:2603.15759, RSS 2026; code CLeARoboticsLab/simdist, MIT).
Status: placeholder — generation not yet run. This card describes the dataset that will land here.
Task
Insertion of JigV2 (insertive) into BottomEnclosure (receptive), on a UR10e with a custom linear
two-jaw gripper, simulated in IsaacLab. 10 Hz control, 16 s episodes (160 steps), 6-D relative
end-effector pose (Cartesian OSC) plus a binary gripper command.
How it was generated
SimDist Algorithm 2. Per environment, a diagonal action-noise covariance is sampled; on each reset the
environment is reassigned to the expert policy with probability 0.5, else to a uniformly drawn
sub-optimal checkpoint. Gaussian action perturbations are injected in contiguous bursts of U[1,5]
steps interleaved with clean stretches of U[5,10] steps (the paper's manipulation intervals). The
binary gripper dimension is perturbed by flipping the commanded open/close rather than by additive
noise, since additive noise on a thresholded signal has no controllable effect.
The point of the perturbations is coverage: a planner searches far outside the expert distribution, so the dataset must contain mistakes, recoveries and failures, not just clean expert trajectories. The paper's own ablation shows expert-only data collapses success from 0.90 to 0.10.
Expert policy, checkpoint ladder and value function come from UR10e-LinearGripper-Jig-BottomEnclosure-Stage1; reset states from UR10e-LinearGripper-Jig-BottomEnclosure-Resets.
Contents
One HDF5 file per shard. All arrays share a leading timestep axis.
| Field | Type | Notes |
|---|---|---|
proprio |
float32 (N, 20) |
last gripper action (1), last arm action (6), arm joint positions (6), end-effector pose (6), binary contact (1) |
front_rgb, side_rgb, wrist_rgb |
vlen uint8 (N,) |
JPEG-encoded, quality 90 |
actions |
float32 (N, 7) |
raw policy output, before the environment's action scale |
rewards |
float32 (N,) |
|
values |
float32 (N,) |
V(s) from the expert critic, always the final checkpoint |
expert_flags |
bool (N,) |
true only when on the expert and the action was uncorrupted |
env_ids, episode_ids |
int32 (N,) |
episode boundary markers |
Observations are deliberately non-privileged. No ground-truth object pose appears anywhere in the observation fields — a world-model encoder that could read object pose directly would defeat the entire premise. Privileged simulator state is used only for the reward and the critic.
Images are stored JPEG-encoded rather than raw: raw uint8 is 451,584 B/step against ~30,714 B/step encoded at 224 px, and a float32 representation would be 1.8 MB/step.
Known limitations
- Dataset scale is the axis the method is most sensitive to (paper Table I: 0.90 at full scale, 0.72 at 50 %, 0.06 at 10 %). The realised scale here is recorded below once generation completes.
- The expert was trained against a reset mixture in which
ObjectPartiallyAssembledEEGraspedwas only ~33 % genuinely holding the object, due to a recorder bug since fixed. That cannot be undone and is documented rather than hidden.
Licence
MIT, following upstream SimDist.
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