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Datasets:
teamcraft
/
TeamCraft-Data-Cen

Languages:
English
Size:
10K<n<100K
Tags:
multi-agent
multi-modal
benchmark
minecraft
License:
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The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    TypeError
Message:      'str' object is not a mapping
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 164, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1729, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1686, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1038, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1979, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1975, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1975, in <dictcomp>
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1972, in from_yaml_inner
                  return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
              TypeError: 'str' object is not a mapping

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Dataset Card for TeamCraft-Data-Cen

The TeamCraft dataset is designed to benchmark multi-modal, multi-agent collaboration in Minecraft. It features 55,000 task variants defined by multi-modal prompts and procedurally generated expert demonstrations.

TeamCraft-Data-Cen is the training split for TeamCraft centralized models, such as TeamCraft-VLA-7B-Cen. In this setup, the model has access to first-person RGB views and inventory information for all agents and generates actions for all agents.

The dataset has a size of approximately 127GB, comprising over one million images and a comprehensive training JSON file. It contains 55,000 unique task variants, each accompanied by a demonstration.

Structure

Input:

  • Multi-Modal Prompts: Language instructions interleaved with orthographic view images (top, left, and front) to specify tasks.
  • Observations: First-person RGB views and agent inventories for all agents.
  • History: Historical actions for all agents (if available).

Output:

  • Action Space: High-level actions for all agents.

Tasks

The TeamCraft dataset includes demonstrations for the following tasks:

For detailed task descriptions and task visualization videos, visit the Task Documentation.

  • Building: Construct structures from provided blueprints.
  • Clearing: Remove specified blocks from target regions.
  • Farming: Sow and harvest crops on designated farmland.
  • Smelting: Use furnaces to smelt resources into goal items.

Applications

This dataset supports research in:

  • Multi-agent reinforcement learning.
  • Multi-modal learning and task planning.
  • Embodied AI in collaborative environments.

Usage

Model training and evaluation available on TeamCraft GitHub Repository.

Disclaimer

This dataset is provided for research purposes only. Users are responsible for ensuring compliance with applicable laws and regulations. The creators do not guarantee the dataset's suitability for any specific purpose and are not liable for any consequences arising from its use.

License

This dataset is released under the Apache License 2.0.

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