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: CastError
Message: Couldn't cast
env_info: struct<env_id: string, env_kwargs: struct<robot_uids: string, obs_mode: string, render_mode: string, (... 147 chars omitted)
child 0, env_id: string
child 1, env_kwargs: struct<robot_uids: string, obs_mode: string, render_mode: string, control_mode: string, sim_backend: (... 85 chars omitted)
child 0, robot_uids: string
child 1, obs_mode: string
child 2, render_mode: string
child 3, control_mode: string
child 4, sim_backend: string
child 5, render_backend: string
child 6, sensor_configs: struct<width: int64, height: int64>
child 0, width: int64
child 1, height: int64
child 2, max_episode_steps: int64
commit_info: null
episodes: list<item: struct<episode_id: int64, episode_seed: int64, control_mode: string, elapsed_steps: int64 (... 67 chars omitted)
child 0, item: struct<episode_id: int64, episode_seed: int64, control_mode: string, elapsed_steps: int64, reset_kwa (... 55 chars omitted)
child 0, episode_id: int64
child 1, episode_seed: int64
child 2, control_mode: string
child 3, elapsed_steps: int64
child 4, reset_kwargs: struct<options: null, seed: int64>
child 0, options: null
child 1, seed: int64
child 5, success: bool
source_type: string
source_desc: string
kept: int64
task: string
per_seed: list<item: struct<seed: int64, kept: bool, success: string, steps: int64>>
child 0, item: struct<seed: int64, kept: bool, success: string, steps: int64>
child 0, seed: int64
child 1, kept: bool
child 2, success: string
child 3, steps: int64
minutes: double
max_len: int64
robot: string
attempts: int64
mean_len_kept: double
to
{'robot': Value('string'), 'task': Value('string'), 'kept': Value('int64'), 'attempts': Value('int64'), 'max_len': Value('int64'), 'mean_len_kept': Value('float64'), 'minutes': Value('float64'), 'per_seed': List({'seed': Value('int64'), 'kept': Value('bool'), 'success': Value('string'), 'steps': Value('int64')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
env_info: struct<env_id: string, env_kwargs: struct<robot_uids: string, obs_mode: string, render_mode: string, (... 147 chars omitted)
child 0, env_id: string
child 1, env_kwargs: struct<robot_uids: string, obs_mode: string, render_mode: string, control_mode: string, sim_backend: (... 85 chars omitted)
child 0, robot_uids: string
child 1, obs_mode: string
child 2, render_mode: string
child 3, control_mode: string
child 4, sim_backend: string
child 5, render_backend: string
child 6, sensor_configs: struct<width: int64, height: int64>
child 0, width: int64
child 1, height: int64
child 2, max_episode_steps: int64
commit_info: null
episodes: list<item: struct<episode_id: int64, episode_seed: int64, control_mode: string, elapsed_steps: int64 (... 67 chars omitted)
child 0, item: struct<episode_id: int64, episode_seed: int64, control_mode: string, elapsed_steps: int64, reset_kwa (... 55 chars omitted)
child 0, episode_id: int64
child 1, episode_seed: int64
child 2, control_mode: string
child 3, elapsed_steps: int64
child 4, reset_kwargs: struct<options: null, seed: int64>
child 0, options: null
child 1, seed: int64
child 5, success: bool
source_type: string
source_desc: string
kept: int64
task: string
per_seed: list<item: struct<seed: int64, kept: bool, success: string, steps: int64>>
child 0, item: struct<seed: int64, kept: bool, success: string, steps: int64>
child 0, seed: int64
child 1, kept: bool
child 2, success: string
child 3, steps: int64
minutes: double
max_len: int64
robot: string
attempts: int64
mean_len_kept: double
to
{'robot': Value('string'), 'task': Value('string'), 'kept': Value('int64'), 'attempts': Value('int64'), 'max_len': Value('int64'), 'mean_len_kept': Value('float64'), 'minutes': Value('float64'), 'per_seed': List({'seed': Value('int64'), 'kept': Value('bool'), 'success': Value('string'), 'steps': Value('int64')})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ManiSkill Cross-Embodiment 3×5
Demonstrations for a cross-embodiment transfer benchmark built on ManiSkill 3: three robot arms × five table-top tasks, with one (robot, task) cell per robot held out (no data). A policy trained on the 12 training cells is tested on the 3 held-out cells, where the task was only ever demonstrated by the other robots.
| PickCube | PushCube | PullCube | StackCube | PlaceSphere | |
|---|---|---|---|---|---|
| Panda | train | train | train | held-out | train |
| XArm6-Robotiq | train | held-out | train | train | train |
| WidowX-AI | held-out | train | train | train | train |
All three robots share the same scene, the same policy camera and the same 7-D action (end-effector delta position + delta rotation + gripper). This dataset contains the 12 training cells only.
Contents
demos/<robot>/<task>/scripted200/
trajectory.h5 200 successful episodes (traj_0 ... traj_199)
trajectory.json ManiSkill metadata: env kwargs, per-episode seed, length, success
summary.json generation log: every attempted seed, kept or not, episode length
matrix3x5_spec.json training spec (path, robot, instruction per cell)
2,400 episodes, 147,817 transitions, about 2.5 GB.
| Robot | Task | Episodes | Attempts | Mean length (steps) |
|---|---|---|---|---|
| panda | PickCube-v1 | 200 | 200 | 35.1 |
| panda | PushCube-v1 | 200 | 204 | 55.5 |
| panda | PullCube-v1 | 200 | 200 | 54.1 |
| panda | PlaceSphere-v1 | 200 | 200 | 72.2 |
| xarm6_robotiq | PickCube-v1 | 200 | 200 | 35.8 |
| xarm6_robotiq | PullCube-v1 | 200 | 200 | 54.6 |
| xarm6_robotiq | StackCube-v1 | 200 | 203 | 81.3 |
| xarm6_robotiq | PlaceSphere-v1 | 200 | 200 | 74.4 |
| widowxai | PushCube-v1 | 200 | 252 | 53.9 |
| widowxai | PullCube-v1 | 200 | 248 | 68.3 |
| widowxai | StackCube-v1 | 200 | 319 | 81.0 |
| widowxai | PlaceSphere-v1 | 200 | 200 | 72.9 |
Per-episode fields (HDF5, ManiSkill RecordEpisode format)
| Key | Shape | Content |
|---|---|---|
obs/sensor_data/base_camera/rgb |
(T+1, 256, 256, 3) uint8 | policy camera (gzip) |
obs/agent/qpos, obs/agent/qvel |
(T+1, dof) | robot joint state |
obs/extra/tcp_pose |
(T+1, 7) | gripper TCP pose (xyz + wxyz quaternion) |
actions |
(T, 7) | pd_ee_delta_pose: xyz delta, rotation delta (always 0 here), gripper |
env_states/actors/<name> |
(T+1, 13) | object poses and velocities (cube, cubeA/cubeB, sphere, bin, goal_region, goal_site) |
success, rewards, terminated, truncated |
(T,) | per-step flags |
Gripper command convention: Panda and WidowX-AI use +1 open / −1 closed; XArm6-Robotiq uses its raw
knuckle target (0 open, 0.81 closed). Instructions per task are in matrix3x5_spec.json.
How the data was generated
- Simulator: ManiSkill 3.0.1, SAPIEN 3.0.3, CPU physics, software rendering, 256 px RGB, control at 20 Hz.
- Policy: one privileged scripted policy per task, identical for all robots. It reads object poses from the simulator and commands world-frame TCP deltas (≤ 4 cm per step) and gripper open/close: approach above the object, descend, grasp, lift, carry, release; Push/Pull drive the gripper from behind the cube with closed-loop re-aiming. The gripper is never rotated. Only successful episodes of ≤ 250 steps are kept.
- Seeds: training seeds start at 0 (0–318). Evaluation scenes in our experiments use seeds 1000+.
- Success: ManiSkill's own success for Push, Pull, Stack and PlaceSphere. For PickCube: cube grasped and lifted ≥ 5 cm (PickCube's goal sphere is hidden from the cameras, so an image-based policy cannot know it).
- WidowX-AI needs three changes that the code applies at runtime (no ManiSkill files edited): an end-effector delta-pose controller (ManiSkill ships joint control only), both gripper carriages driven symmetrically, and a table mount at x = −0.35 m (at ManiSkill's default −0.615 m it cannot reach the shared object region). PickCube uses Panda's configuration (cube size, spawn region, camera) for all robots.
To reproduce the WidowX-AI setup when replaying or evaluating, use scripts/embodiments.py from the code below.
Code
Generation, training (UniVLA and object-centric UniVLA) and evaluation code:
branch object-centric of https://github.com/anh147/lasct
(scripts/gen_scripted_demos.py, scripts/slurm/matrix_train.sh, scripts/slurm/matrix_eval.sh).
from huggingface_hub import snapshot_download
path = snapshot_download("tklee-3012/cross-embodiment", repo_type="dataset")
# put <path>/demos where the code expects demos/ (or symlink), then train with configs/matrix3x5_spec.json
Licence
Rendered from ManiSkill (Apache-2.0) scenes and robot assets (Franka Panda, UFactory XArm6 with Robotiq 2F-85, Trossen WidowX-AI). Check the licences of the robot assets before redistribution.
- Downloads last month
- -