---
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