Dataset Viewer
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: TypeError
Message: Couldn't cast array of type
struct<entrance: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>>>, fresh_section: list<item: struct<id: string, class: string, name: string, pickable: bool, is_device: bool, open_state: string, device_state: string, properties: list<item: string>, states: struct<price: double>, in: string, on: string>>, daily_aisles: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<price: double>, on: string>>, checkout: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>, is_device: bool, device_state: string, on: string>>, storage: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<price: double>, open_state: string, in: string>>, customer_service: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>, on: string>>>
to
{'living_room': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {}, 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string')}), 'kitchen': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string'), 'condition': Value('string'), 'amount': Value('string')}, 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string'), 'open_state': Value('string'), 'in': Value('string')}), 'bedroom': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'on': Value('string'), 'open_state': Value('string'), 'in': Value('string')}), 'balcony': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'is_device': Value('bool'), 'open_state': Value('string'), 'device_state': Value('string'), 'properties': List(Value('string')), 'states': {'condition': Value('string'), 'amount': Value('string')}, 'on': Value('string')}), 'bathroom': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'open_state': Value('string'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'in': Value('string'), 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string')}), 'entrance': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'on': Value('string')})}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<entrance: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>>>, fresh_section: list<item: struct<id: string, class: string, name: string, pickable: bool, is_device: bool, open_state: string, device_state: string, properties: list<item: string>, states: struct<price: double>, in: string, on: string>>, daily_aisles: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<price: double>, on: string>>, checkout: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>, is_device: bool, device_state: string, on: string>>, storage: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<price: double>, open_state: string, in: string>>, customer_service: list<item: struct<id: string, class: string, name: string, pickable: bool, properties: list<item: string>, states: struct<>, on: string>>>
to
{'living_room': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {}, 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string')}), 'kitchen': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string'), 'condition': Value('string'), 'amount': Value('string')}, 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string'), 'open_state': Value('string'), 'in': Value('string')}), 'bedroom': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'on': Value('string'), 'open_state': Value('string'), 'in': Value('string')}), 'balcony': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'is_device': Value('bool'), 'open_state': Value('string'), 'device_state': Value('string'), 'properties': List(Value('string')), 'states': {'condition': Value('string'), 'amount': Value('string')}, 'on': Value('string')}), 'bathroom': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'open_state': Value('string'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'in': Value('string'), 'is_device': Value('bool'), 'device_state': Value('string'), 'on': Value('string')}), 'entrance': List({'id': Value('string'), 'class': Value('string'), 'name': Value('string'), 'pickable': Value('bool'), 'properties': List(Value('string')), 'states': {'cleanliness': Value('string')}, 'on': Value('string')})}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.
YAML Metadata Warning:The task_categories "embodied-agents" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
OpenWorldSandbox Dataset
场景与任务数据（JSON v0.1 规范）——OpenWorldSandbox 具身场景沙盒的官方数据集。
- 代码仓库：https://github.com/ZetingLiu/OpenWorldSandbox（仓库只含代码与规格文档，数据托管于此）
- 沙盒说明：具身场景沙盒，JSON 场景/任务 → compile 为 SQLite 世界快照，17 个语义动作，部分可观测，Goal DSL 判卷。
内容
| 路径 | 说明 |
|---|---|
scenarios/ |
场景包：home_01（家庭，6 区 29 实体）、market_01（超市，6 区 26 实体） |
tasks/ |
11 个任务：home 6 个（含 2 个探索任务变体对）、market 5 个（含 2 个探索任务变体对） |
scenarios/README.md |
场景 JSON 规格 v0.1（冻结） |
tasks/README.md |
任务 JSON 规格 v0.1（冻结） |
任务构成
- 基础任务（7）：导航取放、容器开合、设备操作、复合购物链、清洁/安全扰动任务，均经 walkthrough 回放验证可解。
- 探索任务（4，2026-08 新增）：ARC-AGI-3 风格"先观察再行动"任务——
home_01_laundry_supply_v1/v2：找东西（洗衣液藏在不同容器）market_01_pick_good_apple_v1/v2：候选选择（好/坏苹果对调） 每对变体指令基本相同、环境状态不同，防止"记住固定 ID/路线"通关。
数据生产管线（质量门禁）
- LLM 生成候选 → JSON/schema 校验 → 实体引用检查 → 指令泄漏检查 → 信息获取动作检查 → 动作预算检查 → compile 回放（walkthrough 每步必须成功）→ v1/v2 变体差异检查 → GPT-5 配对结构化审查（固定路线跨版本必须失效）→ 全量回归后才晋级本数据集。
- 原始候选与审查记录见代码仓库
ows/gen/管线（staging 产物不发布）。
使用
from datasets import load_dataset
ds = load_dataset("zetliu2001/OpenWorldSandbox")
或配合沙盒直接编译运行：
ows compile --scenario scenarios/home_01.json --task tasks/home/home_01_umbrella_move.json
ows sandbox --db_path <compiled>.db
版本
- v0.1 规格冻结；数据增量更新（探索任务为 2026-08-26 批次）。
- Downloads last month
- 42