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---
language:
- en
tags:
- text-generation
- machine-learning
license: mit
datasets:
- dart_llm_tasks
---
# 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
```python
# 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 |