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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'reward'}) and 3 missing columns ({'index', 'action', 'next'}).
This happened while the json dataset builder was generating data using
hf://datasets/compsciencelab/BricksRL-Datasets/2Wheeler/RunAway/expert_data/next/meta.json (at revision c7b763b99755b944b0db92728f078360f3ef6543)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
observation: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
reward: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
done: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
distance: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
terminated: struct<device: string, shape: list<item: int64>, dtype: string, is_nested: bool>
child 0, device: string
child 1, shape: list<item: int64>
child 0, item: int64
child 2, dtype: string
child 3, is_nested: bool
shape: list<item: int64>
child 0, item: int64
device: string
_type: string
to
{'observation': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'distance': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'done': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'terminated': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'action': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'next': {'type': Value(dtype='string', id=None)}, 'index': {'device': Value(dtype='string', id=None), 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'dtype': Value(dtype='string', id=None), 'is_nested': Value(dtype='bool', id=None)}, 'shape': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'device': Value(dtype='string', id=None), '_type': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1396, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1045, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2015, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'reward'}) and 3 missing columns ({'index', 'action', 'next'}).
This happened while the json dataset builder was generating data using
hf://datasets/compsciencelab/BricksRL-Datasets/2Wheeler/RunAway/expert_data/next/meta.json (at revision c7b763b99755b944b0db92728f078360f3ef6543)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
observation
dict | distance
dict | done
dict | terminated
dict | action
dict | next
dict | index
dict | shape
sequence | device
string | _type
string | reward
dict |
|---|---|---|---|---|---|---|---|---|---|---|
{
"device": "cpu",
"shape": [
1987,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1987
],
"dtype": "torch.int64",
"is_nested": false
} | [
1987
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1987,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1987
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1987,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
1612,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1612
],
"dtype": "torch.int64",
"is_nested": false
} | [
1612
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1612,
5
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1612
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1612,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
2
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
5000
],
"dtype": "torch.int64",
"is_nested": false
} | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
2
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
5000
],
"dtype": "torch.int64",
"is_nested": false
} | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
5000,
6
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
5000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
5000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
1297
],
"dtype": "torch.int64",
"is_nested": false
} | [
1297
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
1297,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
1297
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
1297,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
10000
],
"dtype": "torch.int64",
"is_nested": false
} | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
9244,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
9244
],
"dtype": "torch.int64",
"is_nested": false
} | [
9244
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
9244,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
9244
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
9244,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
{
"device": "cpu",
"shape": [
10000,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
4
],
"dtype": "torch.float32",
"is_nested": false
} | {
"type": "TensorDict"
} | {
"device": "cpu",
"shape": [
10000
],
"dtype": "torch.int64",
"is_nested": false
} | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | null |
{
"device": "cpu",
"shape": [
10000,
7
],
"dtype": "torch.float32",
"is_nested": false
} | null | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.bool",
"is_nested": false
} | null | null | null | [
10000
] | cpu | <class 'tensordict._td.TensorDict'> | {
"device": "cpu",
"shape": [
10000,
1
],
"dtype": "torch.float32",
"is_nested": false
} |
BricksRL Dataset Card
Dataset Summary
The BricksRL dataset contains curated data for three robotic configurations: 2Wheeler, Walker, and RoboArm. The dataset includes expert and random data for four key tasks: Walker-v0, RoboArm-v0, RunAway-v0, and Spinning-v0. The expert data was collected using a trained Soft Actor-Critic (SAC) agent, while the random data was generated by executing a random policy. This dataset is presented in the paper BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO (NeurIPS 2024). For more information feel free to check out the project website.
Supported Tasks
The dataset supports the following tasks across various robot configurations:
- Walker-v0
- RoboArm-v0
- RunAway-v0
- Spinning-v0
Dataset Structure
The dataset contains two types of data:
- Expert Data: Collected by a trained SAC agent solving the tasks on the real robot. The agent was evaluated over 100 episodes for each task, recording all transitions.
- Random Data: Generated by executing a random policy on the real robot for 100 episodes per task.
The datasets are TensorDicts, which can be directly loaded into the replay buffer. When initiating (pre-)training, provide the path to the desired TensorDict when prompted to load the replay buffer. Table 1 shows the dataset statistics regarding mean reward (expert data), number of transitions collected, and collection episodes.
Results and Evaluation
The dataset was used to train both online and offline RL algorithms (Table 2). Performance comparisons between these methods demonstrated the effectiveness of offline RL algorithms, particularly when using expert data. Online RL algorithms struggled to generalize or often overfit when provided with expert demonstrations. For more detailed information about the hyperparameters, please refer to the appendix of the paper.
Citation
If you use the BricksRL dataset in your research, please cite the following paper:
@article{dittert2024bricksrl,
title={BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO},
author={Sebastian Dittert and Vincent Moens and Gianni De Fabritiis},
journal={arXiv preprint arXiv:2406.17490},
year={2024}
}
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