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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
Task instruction: string
Subtask list: list<item: string>
  child 0, item: string
Robot: string
Scene: string
Object: list<item: list<item: string>>
  child 0, item: list<item: string>
      child 0, item: string
Start: struct<robot_1: string>
  child 0, robot_1: string
Role: int64
Floor: int64
robot_1: struct<Task instruction: string, Subtask list: list<item: string>, Robot: string, Object: list<item: (... 56 chars omitted)
  child 0, Task instruction: string
  child 1, Subtask list: list<item: string>
      child 0, item: string
  child 2, Robot: string
  child 3, Object: list<item: list<item: string>>
      child 0, item: list<item: string>
          child 0, item: string
  child 4, Start: string
  child 5, Task stage: string
G_Rate: double
Type: string
robot_2: struct<Task instruction: string, Subtask list: list<item: string>, Robot: string, Object: list<item: (... 56 chars omitted)
  child 0, Task instruction: string
  child 1, Subtask list: list<item: string>
      child 0, item: string
  child 2, Robot: string
  child 3, Object: list<item: list<item: string>>
      child 0, item: list<item: string>
          child 0, item: string
  child 4, Start: string
  child 5, Task stage: string
R1_Rate: double
to
{'robot_2': {'Task instruction': Value('string'), 'Subtask list': List(Value('string')), 'Robot': Value('string'), 'Object': List(List(Value('string'))), 'Start': Value('string'), 'Task stage': Value('string')}, 'robot_1': {'Task instruction': Value('string'), 'Subtask list': List(Value('string')), 'Robot': Value('string'), 'Object': List(List(Value('string'))), 'Start': Value('string'), 'Task stage': Value('string')}, 'Scene': Value('string'), 'Floor': Value('int64'), 'Type': Value('string'), 'G_Rate': Value('float64'), 'R1_Rate': Value('float64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              Task instruction: string
              Subtask list: list<item: string>
                child 0, item: string
              Robot: string
              Scene: string
              Object: list<item: list<item: string>>
                child 0, item: list<item: string>
                    child 0, item: string
              Start: struct<robot_1: string>
                child 0, robot_1: string
              Role: int64
              Floor: int64
              robot_1: struct<Task instruction: string, Subtask list: list<item: string>, Robot: string, Object: list<item: (... 56 chars omitted)
                child 0, Task instruction: string
                child 1, Subtask list: list<item: string>
                    child 0, item: string
                child 2, Robot: string
                child 3, Object: list<item: list<item: string>>
                    child 0, item: list<item: string>
                        child 0, item: string
                child 4, Start: string
                child 5, Task stage: string
              G_Rate: double
              Type: string
              robot_2: struct<Task instruction: string, Subtask list: list<item: string>, Robot: string, Object: list<item: (... 56 chars omitted)
                child 0, Task instruction: string
                child 1, Subtask list: list<item: string>
                    child 0, item: string
                child 2, Robot: string
                child 3, Object: list<item: list<item: string>>
                    child 0, item: list<item: string>
                        child 0, item: string
                child 4, Start: string
                child 5, Task stage: string
              R1_Rate: double
              to
              {'robot_2': {'Task instruction': Value('string'), 'Subtask list': List(Value('string')), 'Robot': Value('string'), 'Object': List(List(Value('string'))), 'Start': Value('string'), 'Task stage': Value('string')}, 'robot_1': {'Task instruction': Value('string'), 'Subtask list': List(Value('string')), 'Robot': Value('string'), 'Object': List(List(Value('string'))), 'Start': Value('string'), 'Task stage': Value('string')}, 'Scene': Value('string'), 'Floor': Value('int64'), 'Type': Value('string'), 'G_Rate': Value('float64'), 'R1_Rate': Value('float64')}
              because column names don't match

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DeCoNav Dataset

This repository contains the complete compact DeCoNav task release, split into 1,113 training tasks and 100 fixed evaluation tasks. It includes the task annotations and compact evaluator trajectories required by the DeCoNav data and evaluation pipeline. RGB/depth frame dumps, simulator scenes, debug logs, and machine-specific files are intentionally excluded.

Splits

Split Tasks Purpose
train/ 1,113 DeCoNav training-task pool after excluding the fixed test tasks.
test/ 100 Fixed task order used by the released DeCoNav evaluation.

Each task directory contains:

single_config.json
collab_json/collab_config_00.json
success/trial_*/collab_00/task.json

The test directory names are identical to the original 100-task release. Some training instructions occur in multiple scenes or source batches. Those collisions receive a deterministic .__batch_XX suffix so that no task is overwritten. splits.json records the original task name, source batches, selected successful trial, file sizes, and SHA-256 hashes.

Download

hf download Sunyao/deconav --repo-type dataset --local-dir assets/datasets/deconav

Use assets/datasets/deconav/test as the evaluator dataset directory. The DeCoNav source release may pin an older immutable dataset revision for exact paper-result reproduction; that revision remains available in repository history.

Data Scope

  • Simulator: Habitat-Sim
  • Scene identifiers: HM3D v0.2
  • Scene files: not included
  • Released modalities: JSON task annotations and compact trajectories
  • Trial policy: preserve the original released trial for test tasks; use the lexicographically latest successful trial for training tasks when multiple successful trials exist

Evaluator-only positions, geodesic distances, and reference paths stored in the compact trajectory are used only for initialization and metric calculation. They are not policy inputs in the released DeCoNav evaluator.

Integrity

DATA_SHA256SUMS contains hashes for every task file. splits.json contains the same per-file hashes plus split-level and whole-dataset tree hashes. Tree hashes are computed from sorted UTF-8 lines of the form <relative_path>\t<size_bytes>\t<file_sha256>\n.

License

See LICENSE. HM3D scenes are not redistributed and remain subject to their original terms. Access to this dataset does not grant a license to HM3D.

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