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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

dataset_infos.json

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-{
-    "name": "dart_llm_tasks",
-    "version": "1.0.0",
-    "description": "A dataset of natural language instructions for robotic task planning and coordination, designed for few-shot learning evaluation",
-    "size": 102,
-    "features": {
-      "task_id": {
-        "_type": "string",
-        "description": "Unique identifier for the task in format 'dart_llm-{level}-{type}-{number}'"
-      },
-      "text": {
-        "_type": "string",
-        "description": "Natural language instruction for the robotic task"
-      },
-      "output": {
-        "_type": "dict",
-        "description": "Structured task decomposition and execution plan"
-      }
-    },
-    "task_types": {
-      "L1": {
-        "description": "Basic tasks (single robot operations or simple commands)",
-        "count": 47,
-        "examples": ["Move robot to location", "Perform single excavation", "Simple avoidance"]
-      },
-      "L2": {
-        "description": "Medium complexity tasks (two-robot coordination)",
-        "count": 33,
-        "examples": ["Coordinated excavation and transport", "Sequential operations"]
-      },
-      "L3": {
-        "description": "Complex tasks (multi-robot coordination and complex sequences)",
-        "count": 22,
-        "examples": ["Multi-robot simultaneous operations", "Complex area coverage"]
-      }
-    },
-    "available_robots": {
-      "excavators": ["excavator_01", "excavator_02", "excavator_03"],
-      "dump_trucks": ["dump_truck_01", "dump_truck_02", "dump_truck_03"]
-    },
-    "instruction_functions": {
-      "target_area_for_specific_robots": "Move specific robots to target area",
-      "target_area_for_all_robots": "Move all robots to target area",
-      "avoid_areas_for_specific_robots": "Make specific robots avoid areas",
-      "avoid_areas_for_all_robots": "Make all robots avoid areas",
-      "Excavation": "Perform excavation operation",
-      "ExcavatorUnloading": "Unload excavator materials",
-      "DumpLoading": "Load dump truck with materials",
-      "DumpUnloading": "Unload dump truck materials"
-    }
-  }