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Fanqi-Lin
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Processed-Task-Dataset

Tasks:
Robotics
Size:
100B<n<1T
ArXiv:
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code
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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:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 90, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 68, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2068, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 272, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 73, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 826, in read_with_retries
                  out = read(*args, **kwargs)
                File "/usr/local/lib/python3.9/codecs.py", line 322, in decode
                  (result, consumed) = self._buffer_decode(data, self.errors, final)
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

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Robotic Manipulation Datasets for Four Tasks

[Project Page] [Paper] [Code] [Models] [Raw GoPro Videos]

This repository contains in-the-wild robotic manipulation datasets collected using UMI, and processed through a SLAM pipeline, as described in the paper "Data Scaling Laws in Imitation Learning for Robotic Manipulation". The datasets cover four tasks:

  • Pour Water
  • Arrange Mouse
  • Fold Towel
  • Unplug Charger

Dataset Folders:

arrange_mouse and pour_water: Each folder contains data from 32 unique environment-object pairs, with 120 demonstrations per pair.

fold_towel and unplug_charger: Each folder contains data from 32 unique environment-object pairs, with 60 demonstrations per pair.

pour_water_16_env_4_object and arrange_mouse_16_env_4_object: These folders contain data from 16 environments, with 4 different manipulation objects per environment, and 120 demonstrations per object.

Note that due to the size of the pour_water_16_env_4_object/dataset.zarr.zip file (over 50GB), it has been split into two parts. You can restore the full dataset using the following command:

cat pour_water_16_env_4_object/dataset_part_* > pour_water_16_env_4_object/dataset.zarr.zip

Additional Information

  • Each dataset is a merge of smaller datasets (one per environment-object pair). Inside each folder, you will find a count.txt file that lists the number of demonstrations in each smaller dataset.
  • These datasets can be used to train policies that generalize effectively to novel environments and objects.
  • For more details on how to use our datasets, please refer to our code.
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