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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 2 new columns ({'goal', 'goal_rendered'})

This happened while the csv dataset builder was generating data using

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'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/posthoc/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/spherical/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h1/rlpd/stereographic/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/none/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/posthoc/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/spherical/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed0/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed1/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed2/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed3/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed4/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed5/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed6/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed7/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed8/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/ds_experiments/cube-triple-play-singletask/task5/h5/rlpd/stereographic/seed9/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/reproduce/QCRLPD_H5_10M/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/reproduce/QC_H5_1M/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/reproduce/RLPDAC_H5_10M/eval.csv', 'hf://datasets/SZUEAILab/ds-experiments@6d7cd8f3f69ff928dc6619d52e96839a22c7f703/reproduce/RLPD_H1_1M/eval.csv']

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              avg_gripper_contact_length: double
              num_gripper_contacts: double
              prev_qpos: double
              prev_qvel: double
              qpos: double
              qvel: double
              control: double
              time: double
              success: double
              total.timesteps: double
              episode.final_reward: double
              episode.return: double
              episode.length: double
              episode.duration: double
              goal: double
              goal_rendered: double
              step: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2353
              to
              {'avg_gripper_contact_length': Value('float64'), 'num_gripper_contacts': Value('float64'), 'prev_qpos': Value('float64'), 'prev_qvel': Value('float64'), 'qpos': Value('float64'), 'qvel': Value('float64'), 'control': Value('float64'), 'time': Value('float64'), 'success': Value('float64'), 'total.timesteps': Value('float64'), 'episode.final_reward': Value('float64'), 'episode.return': Value('float64'), 'episode.length': Value('float64'), 'episode.duration': Value('float64'), 'step': Value('int64')}
              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 1361, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 940, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1839, 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 2 new columns ({'goal', 'goal_rendered'})
              
              This happened while the csv dataset builder was generating data using
              
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              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)

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End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

DS Experiments

Direction-Speed 动作分解实验数据集。对应 GitHub 仓库 chainpray/qc。

结构 

cube-triple-play-singletask/
└── {task}/
    └── {horizon}/
        └── {method}/
            └── {ds_mode}/
                └── seed{N}/
                    ├── eval.csv           # step, success, episode.return, ...
                    ├── flags.json         # ds_mode, horizon_length, seed, ...
                    ├── online_agent.csv   # critic_loss, actor_loss, alpha, ...
                    ├── env.csv            # qpos, qvel, time, ...
                    └── params_*.pkl       # 模型 checkpoint（仅 seed0）

目录

目录 内容 Seeds Eval Runs
task1/h1/rlpd/ cube-triple-task1 RLPD H=1 4 50 16
task1/h5/rlpd/ cube-triple-task1 RLPD-AC H=5 10 100 40
task1/h5/rlpd-4s/ cube-triple-task1 RLPD-AC H=5（旧） 4 50 16
task2/h1/rlpd/ cube-triple-task2 RLPD H=1 4 50 16
task2/h5/rlpd/ cube-triple-task2 RLPD-AC H=5 4 50 16
task2/h5/fql/ cube-triple-task2 FQL H=5 3 50 6
task3/h1/rlpd/ cube-triple-task3 RLPD H=1 10 100 40
task3/h5/rlpd/ cube-triple-task3 RLPD-AC H=5 10 100 40
task4/h1/rlpd/ cube-triple-task4 RLPD H=1 10 100 40
task4/h5/rlpd/ cube-triple-task4 RLPD-AC H=5 10 100 40
task5/h1/rlpd/ cube-triple-task5 RLPD H=1 10 100 40
task5/h5/rlpd/ cube-triple-task5 RLPD-AC H=5 10 100 40
其他目录 内容 实验数
reproduce/ QC 论文复现实验 4
parallel_benchmark/ 并行训练压力测试 2

命名约定：h1/h5 = horizon length；none = TanhNormal baseline；posthoc/stereographic/spherical = DS 模式。rlpd-4s 为旧版 4-seed 实验，与新 10-seed 版区分。

使用 

from huggingface_hub import hf_hub_download

# 下载 task4 H=5 stereographic seed0 的 eval.csv
hf_hub_download("SZUEAILab/ds-experiments",
    "ds_experiments/cube-triple-play-singletask/task4/h5/rlpd/stereographic/seed0/eval.csv",
    repo_type="dataset")
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