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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"
+    }
+  }