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README.md

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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