EDITING_WORKFLOW_GUIDE.md

📝 Enhanced Editing Workflow Guide

🔧 Fixed Issues

✅ Persistent Editing Problem Solved

• Issue: After updating DAG, editor showed empty {"tasks": []} on subsequent edits
• Solution: Enhanced state management to preserve task plans across edit cycles
• Result: Task plans now persist through the complete workflow

🚀 Complete Workflow

1️⃣ LLM Generation Phase

User Input → LLM Response → DAG Generated → Buttons Appear

Buttons shown: 📝 Edit Task Plan + 🔒 Validate & Deploy Task Plan

2️⃣ Manual Editing Phase (Optional)

Click "📝 Edit Task Plan" → JSON Editor Opens → Make Changes

Buttons shown: 🔄 Update DAG Visualization

3️⃣ DAG Update Phase

Click "🔄 Update DAG Visualization" → New DAG Generated → Review Changes

Buttons shown: 🔒 Validate & Deploy Task Plan

4️⃣ Deployment Phase

Click "🔒 Validate & Deploy Task Plan" → Send to ROS Topics → Confirmation

Status: ✅ Task Plan Successfully Deployed

5️⃣ Re-editing Capability

Click "📝 Edit Task Plan" Again → Previous Plan Loads → Continue Editing

Feature: Deployed plans can be re-edited and updated

🎯 Key Features

🔄 Persistent State Management

• ✅ Task plans persist across edit cycles
• ✅ Deployed plans remain available for re-editing
• ✅ Intelligent fallback to templates when needed
• ✅ Proper error handling for malformed data

📋 Smart Editor Behavior

• With existing plan: Shows current task JSON for editing
• After deployment: Shows deployed plan for potential re-editing
• Empty state: Shows example template with proper structure
• Malformed data: Gracefully falls back to template

🛡️ Error Handling

• JSON Syntax Errors: Shows clear error messages and keeps editor open
• Missing Fields: Validates required structure (tasks field)
• Empty Tasks: Handles empty task arrays gracefully
• State Corruption: Recovers with template when state is invalid

📊 Example JSON Structure

{
  "tasks": [
    {
      "task": "excavate_soil_from_pile",
      "instruction_function": {
        "name": "excavate_soil_from_pile",
        "robot_ids": ["robot_excavator_01"],
        "dependencies": [],
        "object_keywords": ["soil_pile"]
      }
    },
    {
      "task": "transport_soil_to_pit",
      "instruction_function": {
        "name": "transport_soil_to_pit",
        "robot_ids": ["robot_dump_truck_01"],
        "dependencies": ["excavate_soil_from_pile"],
        "object_keywords": ["pit"]
      }
    }
  ]
}

🔍 Testing Commands

Run the Application

cd /root/share/QA_LLM_Module
python3 main.py

Monitor ROS Topics

./monitor_topics.sh

Test Editing Workflow

python3 test_persistent_editing.py

🎉 Success Indicators

✅ Working Correctly When:

1. First Edit: LLM plan loads in editor
2. Update DAG: New visualization appears
3. Deploy: Confirmation message shows
4. Re-edit: Previous plan content loads (not empty template)
5. Multiple Cycles: Can edit → update → deploy repeatedly

🔧 Troubleshooting

Problem: Editor shows empty tasks

• Cause: State management issue
• Solution: Fixed with enhanced persistence logic

Problem: DAG not updating

• Cause: JSON syntax error
• Solution: Check error message, fix JSON, try again

Problem: Cannot deploy

• Cause: ROS node not initialized
• Solution: Restart application, check ROS connection

🏆 Benefits

1. Enhanced Safety: Multiple review opportunities before deployment
2. Flexibility: Manual fine-tuning of LLM-generated plans
3. Persistence: No loss of work during editing cycles
4. User-Friendly: Clear status messages and error handling
5. IEEE Compliant: Professional terminology and workflow