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YongdongWang
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dart_llm_tasks

Languages:
English
Tags:
text-generation
machine-learning
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:    AttributeError
Message:      'str' object has no attribute 'items'
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 1132, in get_module
                  {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1133, in <dictcomp>
                  config_name: DatasetInfo.from_dict(dataset_info_dict)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/info.py", line 284, in from_dict
                  return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
              AttributeError: 'str' object has no attribute 'items'

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DART-LLM Tasks Dataset

Description

DART-LLM Tasks is a dataset designed for evaluating language models in robotic task planning and coordination through few-shot learning. It contains 102 natural language instructions paired with their corresponding structured task decompositions and execution plans.

Dataset Structure

  • Total examples: 102
  • Complexity levels:
    • L1 (Basic): 47 examples
    • L2 (Medium): 33 examples
    • L3 (Complex): 22 examples

Features

  • task_id: Unique identifier for each instruction
  • text: Natural language instruction
  • output: Structured task decomposition and execution plan
    • tasks: List of sub-tasks
      • task: Task name/identifier
      • instruction_function: Function specification
        • name: Function name
        • robot_ids: Involved robots
        • dependencies: Task dependencies
        • object_keywords: Relevant objects/locations

Task Categories

  1. Movement Tasks: Navigation to specific areas
  2. Excavation Tasks: Digging operations
  3. Loading/Unloading: Material transfer
  4. Avoidance Tasks: Obstacle and hazard avoidance
  5. Coordination Tasks: Multi-robot operations

Usage 

# Using the dataset for few-shot evaluation
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("path/to/dart_llm_tasks")

# Access examples
print(dataset[0])  # First example
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