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icl-vfe-eval-results

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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 7 new columns ({'n_episodes', 'n', 'pair', 'value', 'group_id', 'level', 'metric'}) and 14 missing columns ({'gvl_vs_human__pearson', 'robodopamine_zs_vs_human__mae', 'n_frames', 'topreward_vs_human__mae', 'robometer_zs_vs_human__mae', 'topreward_vs_human__pearson', 'topreward_vs_human__kendall_tau_b', 'episode_uid', 'gvl_vs_human__kendall_tau_b', 'gvl_vs_human__mae', 'robodopamine_zs_vs_human__kendall_tau_b', 'robometer_zs_vs_human__pearson', 'robodopamine_zs_vs_human__pearson', 'robometer_zs_vs_human__kendall_tau_b'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Hannibal52Barca/icl-vfe-eval-results/analysis/output/A1-reward model ablation/metrics_long.csv (at revision 904cd931cff9a1953a6ceb604622ac0465a68dcd), ['hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard0.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard1.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard2.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard3.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_source_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_task_source_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/action_chunk_boundaries/chunk_boundaries_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/action_chunk_boundaries/episode_chunk_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_demo/one_task_demo.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_domain_split_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_full_results.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_full_results_common18.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary_common18.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary_common_subset.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_task_method_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/seen-unseen split/seen_unseen_split.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/ICVFE/icvfe_8800_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/ICVFE/icvfe_ema_0.5_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/RECAP/recap_20000_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/SARM/sarm_icl_demo_dataset_subtasks.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/gvl/zs_gvl_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robo_dopamine/finetuned_robodopamine_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/finetuned_robometer_online_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/zeroshot_robometer_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/zeroshot_robometer_online_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/topreward/zs_topreward_icl_demo_dataset_continuous.zip']

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/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 784, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 795, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              level: string
              task: string
              group_id: string
              pair: string
              metric: string
              value: double
              n: int64
              n_episodes: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1165
              to
              {'task': Value('string'), 'episode_uid': Value('string'), 'n_frames': Value('int64'), 'gvl_vs_human__kendall_tau_b': Value('float64'), 'gvl_vs_human__mae': Value('float64'), 'gvl_vs_human__pearson': Value('float64'), 'robodopamine_zs_vs_human__kendall_tau_b': Value('float64'), 'robodopamine_zs_vs_human__mae': Value('float64'), 'robodopamine_zs_vs_human__pearson': Value('float64'), 'robometer_zs_vs_human__kendall_tau_b': Value('float64'), 'robometer_zs_vs_human__mae': Value('float64'), 'robometer_zs_vs_human__pearson': Value('float64'), 'topreward_vs_human__kendall_tau_b': Value('float64'), 'topreward_vs_human__mae': Value('float64'), 'topreward_vs_human__pearson': Value('float64')}
              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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              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 7 new columns ({'n_episodes', 'n', 'pair', 'value', 'group_id', 'level', 'metric'}) and 14 missing columns ({'gvl_vs_human__pearson', 'robodopamine_zs_vs_human__mae', 'n_frames', 'topreward_vs_human__mae', 'robometer_zs_vs_human__mae', 'topreward_vs_human__pearson', 'topreward_vs_human__kendall_tau_b', 'episode_uid', 'gvl_vs_human__kendall_tau_b', 'gvl_vs_human__mae', 'robodopamine_zs_vs_human__kendall_tau_b', 'robometer_zs_vs_human__pearson', 'robodopamine_zs_vs_human__pearson', 'robometer_zs_vs_human__kendall_tau_b'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Hannibal52Barca/icl-vfe-eval-results/analysis/output/A1-reward model ablation/metrics_long.csv (at revision 904cd931cff9a1953a6ceb604622ac0465a68dcd), ['hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A1-reward model ablation/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A2-finetuned vs zeroshot/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/episode_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/metrics_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/task_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/A3-online ft models vs human/total_level_wide.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard0.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard1.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard2.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_full_results_shard3.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_source_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/B2-vision value lambda sweep/b2_task_source_lambda_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/action_chunk_boundaries/chunk_boundaries_long.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/action_chunk_boundaries/episode_chunk_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_demo/one_task_demo.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_domain_split_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_full_results.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_full_results_common18.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary_common18.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_method_summary_common_subset.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/b1_full_run/b1_task_method_summary.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/analysis/output/seen-unseen split/seen_unseen_split.csv', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/ICVFE/icvfe_8800_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/ICVFE/icvfe_ema_0.5_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/RECAP/recap_20000_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/SARM/sarm_icl_demo_dataset_subtasks.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/gvl/zs_gvl_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robo_dopamine/finetuned_robodopamine_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/finetuned_robometer_online_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/zeroshot_robometer_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/robometer/zeroshot_robometer_online_icl_demo_dataset_continuous.zip', 'hf://datasets/Hannibal52Barca/icl-vfe-eval-results@904cd931cff9a1953a6ceb604622ac0465a68dcd/data/demo_set_annotations/demo_set_annotations/topreward/zs_topreward_icl_demo_dataset_continuous.zip']
              
              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.

task
string
episode_uid
string
n_frames
int64
gvl_vs_human__kendall_tau_b
float64
gvl_vs_human__mae
float64
gvl_vs_human__pearson
float64
robodopamine_zs_vs_human__kendall_tau_b
float64
robodopamine_zs_vs_human__mae
float64
robodopamine_zs_vs_human__pearson
float64
robometer_zs_vs_human__kendall_tau_b
float64
robometer_zs_vs_human__mae
float64
robometer_zs_vs_human__pearson
float64
topreward_vs_human__kendall_tau_b
float64
topreward_vs_human__mae
float64
topreward_vs_human__pearson
float64
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000001
584
0.340677
0.221142
0.670354
0.706147
0.174378
0.833689
0.471387
0.216936
0.59686
0.93299
0.311485
0.805652
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000002
535
0.512021
0.350788
0.550193
0.600192
0.142686
0.866757
0.321507
0.228902
0.42677
0.935484
0.143468
0.971598
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000003
418
0.182646
0.34919
0.342701
0.600454
0.141401
0.86696
0.232829
0.241672
0.376069
0.859645
0.081051
0.954158
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000004
427
0.284535
0.271911
0.40359
0.77131
0.103456
0.930612
0.29264
0.227142
0.316955
0.979605
0.143797
0.978772
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000005
456
0.343
0.212841
0.529452
0.753266
0.129439
0.915579
0.40252
0.201252
0.516024
0.885946
0.149751
0.976179
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000006
468
0.408958
0.252388
0.58212
0.635492
0.160908
0.802179
0.46463
0.19142
0.580563
0.88031
0.142645
0.941017
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000007
461
0.254174
0.294901
0.417061
0.859208
0.099281
0.958687
0.177922
0.258271
0.340376
0.924223
0.198602
0.952494
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000008
429
-0.085902
0.36861
-0.100749
0.878398
0.084733
0.961094
0.258845
0.23416
0.391594
0.955946
0.155986
0.977728
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000009
434
0.365388
0.266784
0.623705
0.689424
0.105713
0.924688
0.042153
0.267364
0.219845
0.949693
0.101815
0.986459
hit the eggplant with the mallet
hit_the_eggplant_20260803_133316:000010
440
0.365201
0.225938
0.508687
0.641746
0.11724
0.899823
0.073213
0.25059
0.175485
0.944147
0.115749
0.957965
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000000
654
-0.030654
0.273716
0.004495
0.76073
0.099548
0.91687
0.538536
0.128905
0.718946
0.845028
0.289209
0.77726
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000001
675
0.146476
0.302827
0.394441
0.448604
0.288384
0.65346
0.369681
0.315033
0.542682
0.7642
0.541045
0.54851
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000002
788
0.192254
0.395966
0.161578
0.624889
0.222903
0.879276
0.582803
0.329781
0.653021
0.740176
0.567254
0.547943
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000003
803
0.166517
0.39376
0.163684
0.497113
0.334143
0.707427
0.641597
0.30807
0.758964
0.694209
0.630708
0.475265
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000004
773
0.403029
0.22251
0.478118
0.546648
0.224969
0.889126
0.676802
0.311685
0.910903
0.703401
0.583764
0.44349
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000005
835
0.331156
0.264832
0.286214
0.426509
0.316019
0.580991
0.395582
0.321347
0.654162
0.692403
0.650009
0.413223
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000006
626
0.221561
0.369131
0.289064
0.534472
0.276005
0.789066
0.355321
0.318728
0.536288
0.676149
0.529368
0.544856
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000007
540
0.257563
0.26585
0.405115
0.632361
0.241925
0.845523
0.546411
0.296146
0.714293
0.742771
0.502401
0.596749
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000008
495
0.11394
0.509647
0.198538
0.421745
0.315171
0.573907
0.28824
0.378037
0.259796
0.698566
0.47052
0.664079
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000009
778
-0.366146
0.649942
-0.413341
0.169568
0.356724
0.304053
0.547888
0.323189
0.81482
0.677483
0.631931
0.409297
hit the yellow cube with the mallet using the left arm
hit_the_yellow_cube_20260806_100010:000010
640
0.382091
0.169376
0.713474
0.654343
0.308752
0.73438
0.524125
0.248853
0.934767
0.720537
0.575678
0.457157
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000001
652
0
0.318252
0
0.842069
0.236956
0.851525
0.220273
0.229059
0.28038
0.871614
0.455533
0.71733
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000002
675
0.563031
0.269541
0.713712
0.755964
0.188978
0.85915
-0.150467
0.301802
-0.092101
0.764584
0.37853
0.730585
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000003
450
0.069416
0.381437
0.018347
0.824663
0.145638
0.909772
0.739962
0.245468
0.839258
0.898548
0.355092
0.796732
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000004
862
0.22571
0.34157
0.187653
0.714211
0.278733
0.793484
0.419549
0.15765
0.587968
0.732081
0.3269
0.803935
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000005
690
-0.225241
0.473214
-0.162617
0.672799
0.239476
0.767493
0.38426
0.133816
0.658361
0.811875
0.380284
0.768976
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000008
448
0.35184
0.278593
0.465435
0.850087
0.23
0.909
0.23758
0.217914
0.471992
0.887391
0.272972
0.899089
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000011
559
0
0.345081
0
0
0.539009
0
0
0.517646
0
0
0.786785
0
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000012
678
-0.184185
0.481888
-0.407279
0.757082
0.26308
0.826582
0.268714
0.166295
0.549739
0.690363
0.482886
0.654914
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000013
1,263
-0.005086
0.267783
-0.038259
0.461411
0.214777
0.747454
0.21923
0.141388
0.270721
0.411306
0.552517
0.6279
open the doctor pepper bottle
open_doctor_pepper_bottle_20260806_123110:000014
1,604
0.024714
0.189336
0.057888
0.497309
0.292236
0.774386
0.435443
0.096148
0.729483
0.349452
0.582559
0.538877
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000002
933
-0.167166
0.440666
-0.185952
0.689575
0.279457
0.838122
0.548164
0.253026
0.656691
0.816653
0.576831
0.579191
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000004
589
0.661733
0.234888
0.507836
0.804997
0.183784
0.841579
0.289219
0.213693
0.417536
0.853048
0.339969
0.858998
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000006
602
0.753477
0.101602
0.897236
0.882661
0.204682
0.929614
0.335658
0.243214
0.464079
0.886061
0.324792
0.856492
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000008
664
0.09921
0.361112
-0.028035
0.865795
0.208199
0.901799
0.010392
0.290983
0.083674
0.856845
0.398984
0.793291
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000009
512
0.528255
0.183756
0.717932
0.805055
0.196534
0.884988
0.085222
0.199848
0.348679
0.921846
0.369656
0.838562
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000012
769
0.584837
0.163852
0.686977
0.759704
0.215699
0.886995
0.437281
0.193832
0.476441
0.843227
0.409653
0.773489
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000015
784
0.687608
0.123956
0.79045
0.748121
0.176148
0.889742
0.729142
0.217564
0.811978
0.812768
0.482327
0.721405
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000016
879
0.458277
0.187775
0.548813
0.740013
0.269371
0.842311
0.493385
0.132926
0.579717
0.752699
0.442187
0.784779
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000020
543
0.43981
0.225728
0.553517
0.802782
0.160844
0.913031
0.685649
0.118372
0.866722
0.852289
0.386672
0.835145
open the gatorade bottle
open_the_gatorade_bottle_20260806_124838:000022
816
0.62363
0.121684
0.77503
0.828988
0.211428
0.908014
0.397131
0.287717
0.416735
0.867731
0.407568
0.847516
open the notebook
open_notebook_20260806_095143:000001
550
0.650924
0.185622
0.792219
0.770309
0.201158
0.883003
0.682535
0.352661
0.627934
0.830054
0.347487
0.793902
open the notebook
open_notebook_20260806_095143:000002
563
0
0.317052
0
0
0.493071
0
0
0.658565
0
0
0.684857
0
open the notebook
open_notebook_20260806_095143:000003
424
0.659192
0.153301
0.79974
0.783149
0.255054
0.853769
0.545173
0.407966
0.54829
0.821716
0.350767
0.760551
open the notebook
open_notebook_20260806_095143:000004
505
0.367483
0.255841
0.573648
0.372656
0.30708
0.659611
0.704815
0.379808
0.651092
0.813205
0.384745
0.73199
open the notebook
open_notebook_20260806_095143:000005
364
0.475221
0.321825
0.503559
0.843522
0.173485
0.911969
0.74249
0.307554
0.764758
0.885032
0.207489
0.92905
open the notebook
open_notebook_20260806_095143:000006
309
0.56982
0.289716
0.606836
0.87885
0.152928
0.933319
0.75024
0.285834
0.808699
0.894478
0.185248
0.950193
open the notebook
open_notebook_20260806_095143:000007
253
0.356005
0.373884
0.387448
0.895716
0.169744
0.935576
0.55937
0.315573
0.673367
0.903888
0.211521
0.933544
open the notebook
open_notebook_20260806_095143:000008
498
0.472088
0.269581
0.621144
0.672219
0.239556
0.791921
0.343534
0.408434
0.460114
0.584624
0.3899
0.593755
open the notebook
open_notebook_20260806_095143:000009
389
0.464897
0.292156
0.390178
0.830784
0.219225
0.887851
0.443319
0.384903
0.499462
0.697323
0.296677
0.724931
open the notebook
open_notebook_20260806_095143:000010
512
0.055034
0.391578
0.262155
0.83818
0.251714
0.840475
0.642928
0.34611
0.608573
0.86687
0.293608
0.835629
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000000
569
0.259252
0.295424
0.42936
0.732147
0.15135
0.854257
0.43086
0.277072
0.551907
0.908791
0.406178
0.69826
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000001
505
0.654442
0.130463
0.882189
0.928412
0.101272
0.966406
0.602547
0.211311
0.811606
0.841019
0.290499
0.870451
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000003
672
0.674347
0.173416
0.856239
0.846192
0.113874
0.950896
0.776519
0.196472
0.875404
0.91395
0.419947
0.772994
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000004
1,245
0.385467
0.200849
0.62767
0.841396
0.098335
0.965921
0.821005
0.117666
0.97665
0.797029
0.404884
0.695304
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000006
370
0.694885
0.187124
0.851131
0.932012
0.120575
0.964723
0.876384
0.15132
0.944069
0.903553
0.215924
0.902881
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000007
1,169
0.665619
0.137551
0.715438
0.731975
0.190959
0.885839
0.719857
0.248667
0.85211
0.851359
0.500473
0.546245
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000008
803
0.433591
0.292122
0.582077
0.896528
0.119561
0.975066
0.840789
0.169011
0.960509
0.910303
0.395912
0.809415
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000009
730
0.516141
0.141559
0.778916
0.821459
0.112599
0.949409
0.784158
0.203096
0.849932
0.940206
0.416759
0.742581
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000010
610
0.494726
0.164513
0.624522
0.904979
0.060302
0.976144
0.773644
0.130536
0.911881
0.89823
0.333572
0.848765
open the notebook and place the cube on it
opening_notebook_and_placing_cube_20260803_141540:000011
469
0.151859
0.347612
0.239943
0.914609
0.095843
0.965837
0.49521
0.231151
0.691759
0.920756
0.291759
0.895705
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000001
477
0.523252
0.207629
0.587616
0.752282
0.141033
0.877571
0.521971
0.237803
0.674812
0.94864
0.310255
0.874868
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000002
559
0
0.446154
0
0.880411
0.138721
0.907255
0.390701
0.190012
0.733665
0.823069
0.35141
0.80133
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000003
517
0.481391
0.22608
0.624263
0.862547
0.149156
0.890682
0.73791
0.169058
0.863094
0.885828
0.306967
0.79799
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000004
570
0.178451
0.304275
0.157728
0.85441
0.140274
0.898141
0.456708
0.220346
0.699994
0.91344
0.345892
0.788281
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000005
489
0.237292
0.256803
0.459076
0.820046
0.137957
0.915869
0.486154
0.187174
0.731709
0.925183
0.341741
0.875343
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000006
387
0.270724
0.335896
0.317279
0.803208
0.122688
0.920062
0.626407
0.174561
0.839876
0.946499
0.248637
0.914856
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000007
451
0.666395
0.205443
0.771788
0.786269
0.141673
0.92368
0.899439
0.139561
0.948333
0.9093
0.400726
0.824269
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000008
383
0.438361
0.277151
0.483417
0.919247
0.123675
0.957321
0.621032
0.213305
0.860784
0.939789
0.264359
0.904732
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000009
486
0.645198
0.180843
0.791758
0.716953
0.109777
0.876735
0.84778
0.104762
0.946399
0.924915
0.325987
0.828242
pass the gusset to the plate
pass_gusset_to_plate_20260803_144204:000010
456
0.670399
0.139354
0.872641
0.776612
0.110389
0.935813
0.671973
0.204761
0.825853
0.870803
0.334034
0.881725
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000000
647
-0.094114
0.492083
-0.140583
0.902613
0.119401
0.97444
0.860846
0.111343
0.962487
0.885928
0.415651
0.758081
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000001
851
0
0.378496
0
0.839363
0.146347
0.932698
0.859655
0.097452
0.917742
0.859772
0.446439
0.687084
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000002
501
0.415587
0.283759
0.516291
0.929718
0.132035
0.948897
0.885764
0.108091
0.950489
0.925507
0.329601
0.845603
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000003
661
0.474019
0.240092
0.436077
0.826967
0.142463
0.94556
0.689307
0.158024
0.668727
0.888499
0.412641
0.782396
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000004
436
0.58321
0.329302
0.708139
0.943345
0.076073
0.9796
0.785443
0.163929
0.909288
0.920842
0.331612
0.857123
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000005
603
0.598508
0.276452
0.659867
0.903294
0.103025
0.970614
0.877576
0.124664
0.918083
0.902425
0.326853
0.804525
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000006
741
-0.01554
0.356964
-0.01941
0.739263
0.148662
0.877762
0.803707
0.154429
0.794686
0.803362
0.437729
0.700808
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000007
457
0
0.413566
0
0.873315
0.137293
0.926471
0.813115
0.184276
0.842072
0.927572
0.311842
0.856611
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000009
530
0.356815
0.260672
0.552589
0.866718
0.131539
0.952914
0.79299
0.153774
0.905843
0.90602
0.388044
0.779863
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000010
478
0.664751
0.148356
0.845744
0.886432
0.172868
0.903135
0.869566
0.152492
0.90215
0.856429
0.35836
0.817598
pass the mustard to the plate
pass_mustard_to_plate_20260803_143010:000011
381
0.247751
0.307339
0.292249
0.846729
0.10554
0.956012
0.921615
0.147912
0.922255
0.924404
0.313637
0.87103
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000000
502
0.082613
0.3515
0.042542
0.898645
0.095825
0.951228
0.792464
0.167356
0.873723
0.785035
0.320576
0.835944
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000002
635
0.015357
0.364653
0.118966
0.782403
0.093834
0.899494
0.264002
0.240728
0.414728
0.741291
0.336664
0.714164
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000004
665
-0.192123
0.41693
-0.242493
0.810769
0.105071
0.901972
0.63511
0.227664
0.729554
0.875092
0.34994
0.780574
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000006
654
0.268903
0.270811
0.241428
0.841268
0.109136
0.905395
0.4668
0.233272
0.415604
0.781795
0.424599
0.747235
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000008
778
0.690643
0.149097
0.744709
0.773175
0.114949
0.910425
0.627431
0.146854
0.789895
0.770834
0.578611
0.555864
pass the salt shaker from the left arm to the right arm
pass_the_salt_shaker_20260806_101215:000010
530
0.142102
0.282665
0.193613
0.8576
0.132147
0.847571
0.231789
0.227711
0.476694
0.862032
0.284261
0.813666
pass the salt shaker from the right arm to the left arm
pass_the_salt_shaker_20260806_101215:000003
511
0.117716
0.269441
0.274939
0.819092
0.149373
0.883314
0.520633
0.143457
0.701283
0.755111
0.418546
0.771511
pass the salt shaker from the right arm to the left arm
pass_the_salt_shaker_20260806_101215:000005
281
0.347158
0.324574
0.448059
0.924925
0.106511
0.92499
0.421776
0.261157
0.626894
0.968555
0.253238
0.875068
pass the salt shaker from the right arm to the left arm
pass_the_salt_shaker_20260806_101215:000007
367
0.612647
0.225898
0.717795
0.91053
0.107073
0.950693
-0.177384
0.276201
0.185858
0.912588
0.325389
0.824295
pass the salt shaker from the right arm to the left arm
pass_the_salt_shaker_20260806_101215:000009
850
-0.009359
0.318547
-0.005661
0.781755
0.147612
0.872413
-0.06643
0.206983
0.22913
0.851292
0.376987
0.727958
pass the salt shaker from the right arm to the left arm
pass_the_salt_shaker_20260806_101215:000011
450
0
0.460001
0
0.830675
0.13333
0.863458
0.278671
0.180707
0.359
0.887181
0.358424
0.806806
pick up the red chilli and place it on the plate with the left arm
pnp_red_chilli_to_plate_20260806_104152:000001
197
0
0.57868
0
0.924566
0.081602
0.984979
0.945143
0.110226
0.989402
0.954808
0.162525
0.925203
pick up the red chilli and place it on the plate with the left arm
pnp_red_chilli_to_plate_20260806_104152:000003
198
0.392602
0.255954
0.654135
0.954139
0.095366
0.979253
0.895097
0.150834
0.960677
0.951081
0.136665
0.963122
pick up the red chilli and place it on the plate with the left arm
pnp_red_chilli_to_plate_20260806_104152:000005
238
0.678797
0.309426
0.82414
0.898905
0.137112
0.965444
0.76767
0.125316
0.962418
0.958839
0.102293
0.972139
pick up the red chilli and place it on the plate with the left arm
pnp_red_chilli_to_plate_20260806_104152:000007
196
0
0.510202
0
0.949841
0.124592
0.973137
0.935032
0.170954
0.95775
0.899456
0.185497
0.948262
pick up the red chilli and place it on the plate with the left arm
pnp_red_chilli_to_plate_20260806_104152:000009
195
0.378516
0.54359
0.34234
0.953384
0.112942
0.976182
0.892561
0.127014
0.98494
0.940677
0.092229
0.980966
pick up the red chilli and place it on the plate with the right arm
pnp_red_chilli_to_plate_20260806_104152:000000
214
0.693941
0.286761
0.853627
0.970415
0.064337
0.9923
0.950981
0.098678
0.98717
0.894134
0.132804
0.963395
pick up the red chilli and place it on the plate with the right arm
pnp_red_chilli_to_plate_20260806_104152:000002
196
0.748532
0.255482
0.844466
0.962963
0.079858
0.989022
0.956111
0.209459
0.926285
0.962782
0.125654
0.971108
End of preview.

ICL Project — Value/Reward Model Evaluation Results

Per-frame progress predictions and evaluation outputs for the value/reward models compared in the VICTR paper (IC-VFE, RECAP, Robometer, Robo-Dopamine, TOPReward, GVL, SARM), on the ICL demo dataset. Code: icvfe-evals.

The paths mirror that repo, so you can download into a checkout and run python analysis/pipeline.py directly (it extracts data/ into analysis/cache/):

git clone https://github.com/HannibalofBarca/icvfe-evals && cd icvfe-evals
hf download Hannibal52Barca/icl-vfe-eval-results --repo-type dataset --local-dir .

Contents

data/demo_set_annotations/demo_set_annotations/: model prediction archives

Folder Archives
ICVFE/ icvfe_8800_… (IC-VFE main, step 8800), icvfe_ema_0.5_… (causal EMA α=0.5)
RECAP/ recap_20000_…
robometer/ zero-shot, zero-shot online, fine-tuned online
robo_dopamine/ fine-tuned
topreward/, gvl/, SARM/ zero-shot TOPReward, GVL, SARM subtasks

Five archives the pipeline also expects are already published in other datasets. Place them at:

Path in data/demo_set_annotations/demo_set_annotations/ Source
manual/manual_icl_demo_dataset_{continuous,milestone}.zip icl_project_manual_annotation
robo_dopamine/zs_robodopamine_icl_demo_dataset_continuous.zip icl_project_robodopamine zero_shot/
robometer/finetuned_robometer_icl_demo_dataset_continuous.zip icl_project_robometer finetuned/
dino/dino_base_icl_demo_dataset_continuous.zip icl_project_dino dino_embeddings_demo.zip (rename)

analysis/output/ (A1–A3 task/total metrics are episode-averaged: computed per episode, then averaged, with IC-VFE context replicates averaged per episode first; constant curves score 0 correlation. The seen/unseen split reports both episode-averaged and frame-pooled values in its aggregation column): result tables and figures (A1–A3 ablations, B1 retrieval full run merged results, B2 λ sweep, seen/unseen split, action-chunk boundaries).

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