Datasets:

RKrohn
/

MiTaS

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type list<item: uint8> to uint8
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 87, in _generate_tables
                  yield Key(file_idx, batch_idx), cast_table_to_features(pa_table, self.info.features)
                                                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2285, in cast_table_to_features
                  arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
                  casted_array_values = _c(array.values, feature.feature)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
                  return array_cast(
                      array,
                  ...<2 lines>...
                      allow_decimal_to_str=allow_decimal_to_str,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2016, in array_cast
                  raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
              TypeError: Couldn't cast array of type list<item: uint8> to uint8

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Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

MiTaS demonstration dataset accompanying the paper.

Project page: https://mitas-touch.github.io/
OpenReview: https://openreview.net/forum?id=b2gSINMX3R
arXiv: https://arxiv.org/abs/2606.06281

@inproceedings{
krohn2026multiresolution,
title={Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation},
author={Rickmer Krohn and Erik Helmut and Niklas Funk and Jan Peters and Vignesh Prasad and Georgia Chalvatzaki},
booktitle={10th Annual Conference on Robot Learning},
year={2026},
url={https://openreview.net/forum?id=b2gSINMX3R}
}

Rickmer Krohn, Erik Helmut, Niklas Funk, Jan Peters, Vignesh Prasad, Georgia Chalvatzaki. Accepted at CoRL 2026.

Dataset

Real-world teleoperated demonstrations for contact-rich manipulation. Each episode is a self-contained HDF5 file with robot actions, proprioception, RGB camera frames, and tactile images (PNG bytes stored inside the file). Some episodes also embed an MP4 of the RGB stream.

Folder	Episodes
`board_wiping`	62
`gear_assembly`	64
`key_inlock`	64
`lamp_install`	62
`lightbulb_connection`	62
`lamp_install_dual_gelsight`	31

The first five tasks use RGB, GelSight Mini, and EveTac. lamp_install_dual_gelsight is an extra lamp-installation set (not in the paper) with two GelSight Mini sensors (gelsight_red, gelsight_yellow) and no EveTac.

345 episodes in total, about 35 GB.

Layout

board_wiping/session_01/episode_000.h5
board_wiping/session_02/episode_000.h5
...
lamp_install_dual_gelsight/episode_000.h5

Episodes are numbered chronologically from the recording time attribute within each session. Session 01 and session 02 are two independent collections of the same five tasks.

Sensors and robot

7-DoF arm with gripper (Polymetis)
RGB camera, history length k=2
GelSight Mini, history length k=2
EveTac, history length k=16

Tactile and RGB frames are stored as PNG byte strings in object datasets, not as decoded arrays.

HDF5 schema

Top-level groups: action/, observation/ (proprio / control), observations/ (sensors). First dimension of numeric datasets is episode length T.

Useful fields:

action/cartesian_position — (T, 6)
action/cartesian_position_delta_ik — (T, 6)
action/joint_position — (T, 7)
action/gripper_position — (T,)
observations/image_png/<camera_id> — (T, 2) PNG bytes
observations/tactile_png/gelsight — (T, 2) PNG bytes
observations/tactile_png/evetac — (T, 16) PNG bytes
observations/tactile_png/gelsight_red / gelsight_yellow — (T, 2)
observations/videos/<camera_id> — raw MP4 bytes when present

File attributes include time, success, camera_history_k, and (on later recordings) sensor_mode.

Load an episode

import io
import h5py
from PIL import Image

path = "board_wiping/session_01/episode_000.h5"

with h5py.File(path, "r") as f:
    print(dict(f.attrs))
    print("T =", f["action/joint_position"].shape[0])
    camera_id = next(iter(f["observations/image_png"].keys()))
    image = Image.open(io.BytesIO(f["observations/image_png"][camera_id][0, 0]))
    tactile = Image.open(io.BytesIO(f["observations/tactile_png/gelsight"][0, 0]))

For the dual-GelSight set, read observations/tactile_png/gelsight_red and gelsight_yellow instead of gelsight / evetac.

License

CC-BY-4.0

Downloads last month: 4

Total file size:

36.7 GB

Paper for RKrohn/MiTaS

Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

Paper • 2606.06281 • Published Jun 4