Gazebo MCP Server

# Phase 7: Demonstrations & Showcase **Status**: 🔵 Not Started **Estimated Duration**: 2-3 weeks **Prerequisites**: Phases 1-4 Complete (Phase 5 enhancements enhance demos) --- ## Quick Reference **What you'll build**: Complete demonstration scenarios, step-by-step guides, and showcase applications that highlight the MCP server's capabilities **Tasks**: 35+ across 5 modules - Module 7.1: Core Demo Scenarios (8 demos) - Module 7.2: Tutorial Series (10 tutorials) - Module 7.3: Live Demo Scripts (6 scripts) - Module 7.4: Showcase Applications (5 apps) - Module 7.5: Presentation Materials (6 materials) **Success criteria**: Can deliver compelling 5-30 minute demos for different audiences, complete tutorial series published, showcase apps demonstrate real-world use cases **Key deliverables**: - ✅ 8 polished demo scenarios with scripts - ✅ 10-part tutorial series (beginner to advanced) - ✅ 6 live demo scripts with timing guides - ✅ 5 showcase applications - ✅ Presentation deck, videos, and assets --- ## Overview **Phase 7** creates compelling demonstrations and educational content that showcase the ROS2 Gazebo MCP Server's capabilities. These materials serve multiple purposes: - **Sales/Marketing**: Demonstrate value to potential users - **Education**: Teach users how to leverage the system - **Validation**: Prove the system works in real scenarios - **Inspiration**: Show what's possible with AI-controlled robotics **Target Audiences**: - Researchers evaluating the platform - Developers learning to use the MCP server - Conference attendees seeing live demos - Students and hobbyists exploring robotics - Decision-makers evaluating ROI --- ## Learning Objectives By completing this phase, you will understand: 1. **Effective Demo Design** - How to structure demos for impact - Timing and pacing for different audiences - Handling failure gracefully during live demos - Creating memorable "wow moments" 2. **Tutorial Creation** - Progressive complexity in teaching - Balancing theory and practice - Creating reproducible examples - Effective documentation techniques 3. **Presentation Skills** - Crafting compelling narratives - Technical storytelling - Visual communication - Audience engagement 4. **Content Production** - Screen recording and editing - Creating visual assets - Writing clear instructions - Building polished deliverables --- ## Core Principles for This Phase ### 1. Show, Don't Tell **Always demonstrate live**: ```markdown ❌ "The system can spawn multiple robots" ✅ [Live demo] "Let me show you - I'll ask Claude to create a fleet of 5 TurtleBot3 robots in a warehouse..." [Robots appear on screen] ``` ### 2. Build Progressive Complexity Start simple, add layers: 1. **Foundation**: Single robot, simple movement 2. **Capability**: Add sensors, obstacle avoidance 3. **Complexity**: Multi-robot coordination 4. **Innovation**: Novel use cases, AI integration ### 3. Prepare for Failures Always have backup plans: - Pre-recorded fallback videos - Simplified demo versions - Multiple test runs beforehand - Quick recovery procedures ### 4. Create Memorable Moments Include at least one "wow" moment per demo: - Autonomous navigation solving complex maze - Multi-robot coordinated behavior - Real-time world generation from AI description - Seamless AI-to-robot control ### 5. Make It Reproducible Every demo should include: - Exact setup instructions - Configuration files - Expected outputs - Troubleshooting guide --- ## Module 7.1: Core Demo Scenarios **Goal**: Create polished, repeatable demonstrations for common use cases ### Tasks (0/8) #### Demo 7.1.1: "Hello World" - First Robot ⏳ **Duration**: 5 minutes **Audience**: First-time users, executives **Wow Factor**: 2/5 **Scenario**: Show the absolute basics - AI controlling a robot in Gazebo. **Script**: ```markdown ## Setup (pre-demo) - Gazebo running with empty world - MCP server connected - Claude Code terminal visible ## Demo Flow (5 minutes) ### Part 1: Introduction (1 min) "Today I'll show you how AI can control robots in simulation through natural language. No coding required." ### Part 2: Spawn Robot (1 min) [To Claude] "Please start a Gazebo simulation and spawn a TurtleBot3 robot at the center of the world." Expected: Robot appears in Gazebo viewer ### Part 3: Basic Control (2 min) [To Claude] "Make the robot drive forward 2 meters, then turn around and come back to the start." Expected: Robot executes commands smoothly ### Part 4: Add Complexity (1 min) [To Claude] "Now add some obstacles around the robot and have it navigate around them back to the origin." Expected: Obstacles spawn, robot navigates autonomously ## Key Talking Points - Natural language control - No manual coding needed - AI understands robotics concepts - Easy simulation setup ## Recovery Plan If robot doesn't move: Use pre-recorded video If spawn fails: Restart with backup world file ``` **Files**: - `demos/01_hello_world/script.md` - `demos/01_hello_world/setup.sh` - `demos/01_hello_world/config.yaml` - `demos/01_hello_world/backup_video.mp4` --- #### Demo 7.1.2: Obstacle Course Challenge ⏳ **Duration**: 10 minutes **Audience**: Technical users, researchers **Wow Factor**: 4/5 **Scenario**: AI generates a random obstacle course and navigates it. **Script**: ```markdown ## Setup - Gazebo with empty world - MCP server with Phase 4 tools available ## Demo Flow (10 minutes) ### Part 1: World Generation (3 min) [To Claude] "Create a challenging obstacle course with 15 obstacles of varying heights. Make it solvable but difficult." Show: Obstacles appear in random but navigable layout ### Part 2: Robot Deployment (2 min) [To Claude] "Spawn a TurtleBot3 Waffle at the start position and show me a top-down view of the entire course." Show: Robot spawns, camera adjusts to show full course ### Part 3: Autonomous Navigation (4 min) [To Claude] "Navigate the robot through this obstacle course to reach the opposite side. Use the LiDAR to avoid obstacles." Show: Robot autonomously navigates, avoiding obstacles ### Part 4: Analysis (1 min) [To Claude] "Give me statistics: time taken, distance traveled, closest approach to any obstacle." Show: Data analysis from sensor logs ## Talking Points - Procedural world generation - Real-time sensor integration - Autonomous planning and control - Data-driven analysis ## Variations - Easy mode: 5 obstacles, wide spacing - Hard mode: 25 obstacles, narrow passages - Timed challenge: Must complete in <60 seconds ``` **Files**: - `demos/02_obstacle_course/script.md` - `demos/02_obstacle_course/timing_guide.md` - `demos/02_obstacle_course/variations.md` --- #### Demo 7.1.3: Multi-Robot Coordination ⏳ **Duration**: 15 minutes **Audience**: Advanced users, research labs **Wow Factor**: 5/5 **Scenario**: Fleet of robots coordinate to solve tasks together. **Script**: ```markdown ## Setup - Large warehouse world - MCP server with Phase 5.8 multi-robot features ## Demo Flow (15 minutes) ### Part 1: Fleet Deployment (3 min) [To Claude] "Spawn 5 TurtleBot3 robots in a grid formation, numbered 1-5, and assign each a unique color." Show: 5 robots appear in formation, different colors ### Part 2: Coordinated Movement (4 min) [To Claude] "Have all robots move in a circular pattern while maintaining equal spacing between them." Show: Robots move in synchronized circle ### Part 3: Task Allocation (4 min) [To Claude] "Place 10 target objects randomly in the warehouse. Have the robots work together to visit all targets, optimizing for minimum total travel distance." Show: Robots split up, each covering different targets ### Part 4: Formation Control (4 min) [To Claude] "Now have the robots form a V-shape formation and navigate together through the warehouse to a designated exit." Show: Robots maintain V-formation while moving ## Talking Points - Swarm coordination - Task allocation algorithms - Formation control - Distributed decision-making ## Advanced Variations - Add dynamic obstacles - Include robot failures (demonstrate fault tolerance) - Mixed robot types (different capabilities) ``` **Files**: - `demos/03_multi_robot/script.md` - `demos/03_multi_robot/world_warehouse.sdf` - `demos/03_multi_robot/formation_configs.yaml` --- #### Demo 7.1.4: Dynamic World Generation ⏳ **Duration**: 12 minutes **Audience**: Game developers, simulation researchers **Wow Factor**: 4/5 **Scenario**: AI generates complete worlds from natural language descriptions. **Script**: ```markdown ## Demo Flow (12 minutes) ### Part 1: Basic World (3 min) [To Claude] "Create a Mars-like environment with red terrain, rocky obstacles, and low gravity." Show: Terrain generates with Mars characteristics ### Part 2: Time of Day (2 min) [To Claude] "Set the time to sunset on Mars and add atmospheric effects." Show: Lighting changes, atmosphere appears ### Part 3: Interactive Elements (3 min) [To Claude] "Add a dust storm that moves across the terrain and affects visibility." Show: Dynamic weather system ### Part 4: Robot Testing (4 min) [To Claude] "Spawn a rover and test its movement in Mars gravity with the dust storm active." Show: Rover moves differently due to gravity, visibility affected ## Talking Points - Procedural world generation - Environmental effects - Physics simulation - Real-time modifications ``` --- #### Demo 7.1.5: Sensor Showcase ⏳ **Duration**: 10 minutes **Audience**: Perception researchers, CV engineers **Scenario**: Demonstrate all sensor types and data access. --- #### Demo 7.1.6: Real-Time Debugging ⏳ **Duration**: 8 minutes **Audience**: Developers, debugging enthusiasts **Scenario**: Show debugging tools and visualization. --- #### Demo 7.1.7: Benchmark Comparison ⏳ **Duration**: 10 minutes **Audience**: Researchers, performance analysts **Scenario**: Compare algorithms using reproducible benchmarks. --- #### Demo 7.1.8: Full Workflow End-to-End ⏳ **Duration**: 20 minutes **Audience**: Technical decision-makers **Scenario**: Complete workflow from idea to deployed solution. --- ## Module 7.2: Tutorial Series **Goal**: Progressive tutorial series from beginner to advanced ### Tasks (0/10) #### Tutorial 7.2.1: "Getting Started" ⏳ **Target**: Complete beginners **Duration**: 30 minutes **Prerequisites**: None **Content**: ```markdown # Tutorial 1: Getting Started with Gazebo MCP ## What You'll Learn - Install ROS2 and Gazebo - Set up the MCP server - Spawn your first robot - Basic movement commands ## Prerequisites - Ubuntu 22.04 or 24.04 - Basic command-line knowledge - No robotics experience needed ## Step 1: Installation (10 minutes) ### Install ROS2 Humble ```bash # Add ROS2 repository sudo apt update sudo apt install software-properties-common sudo add-apt-repository universe # Install ROS2 sudo apt update sudo apt install ros-humble-desktop ``` ### Install Gazebo Harmonic ```bash sudo apt install gz-harmonic ``` ### Install MCP Server ```bash git clone https://github.com/yourusername/gazebo-mcp.git cd gazebo-mcp/ros2_gazebo_mcp pip install -e . ``` ## Step 2: First Launch (5 minutes) ### Start Gazebo ```bash source /opt/ros/humble/setup.bash gz sim ``` ### Start MCP Server ```bash # In new terminal gazebo-mcp-server ``` ## Step 3: Your First Robot (10 minutes) ### Connect with Claude [Open Claude Code] Prompt: "I want to spawn a TurtleBot3 robot in Gazebo." Expected response: ``` I'll help you spawn a TurtleBot3 robot. Using the spawn_model tool... [Robot appears in Gazebo] ``` ### Make It Move Prompt: "Make the robot drive forward 1 meter." Watch the robot move! ## Step 4: Explore Capabilities (5 minutes) Try these commands: - "Turn the robot 90 degrees to the right" - "Drive in a square pattern" - "Get the robot's current position" ## Troubleshooting **Problem**: MCP server won't start **Solution**: Check ROS2 is sourced: `echo $ROS_DISTRO` **Problem**: Robot doesn't appear **Solution**: Check Gazebo is running: `gz topic -l` ## Next Steps - Tutorial 2: Working with Sensors - Tutorial 3: Creating Worlds - Experiment with different robot types ``` **Files**: - `tutorials/01_getting_started/README.md` - `tutorials/01_getting_started/installation_checker.sh` - `tutorials/01_getting_started/quick_test.py` --- #### Tutorial 7.2.2: "Working with Sensors" ⏳ **Target**: Beginners **Duration**: 45 minutes **Content**: Camera, LiDAR, IMU access and visualization --- #### Tutorial 7.2.3: "Creating Custom Worlds" ⏳ **Target**: Intermediate **Duration**: 60 minutes **Content**: World file basics, object placement, lighting --- #### Tutorial 7.2.4: "Multi-Robot Scenarios" ⏳ **Target**: Intermediate-Advanced **Duration**: 60 minutes **Content**: Spawning multiple robots, coordination, namespaces --- #### Tutorial 7.2.5: "Path Planning Basics" ⏳ #### Tutorial 7.2.6: "Sensor Fusion Techniques" ⏳ #### Tutorial 7.2.7: "Performance Optimization" ⏳ #### Tutorial 7.2.8: "Custom Tool Development" ⏳ #### Tutorial 7.2.9: "Integration with Nav2" ⏳ #### Tutorial 7.2.10: "Production Deployment" ⏳ --- ## Module 7.3: Live Demo Scripts **Goal**: Timed scripts for conference talks and live presentations ### Tasks (0/6) #### Script 7.3.1: 5-Minute Lightning Talk ⏳ **Format**: Conference lightning talk **Objective**: Generate interest and awareness **Structure**: ```markdown ## Timing Breakdown (5:00 total) ### Slide 1: Title (0:00-0:30) "AI-Controlled Robotics: From Natural Language to Robot Actions" ### Slide 2: The Problem (0:30-1:00) "Current challenge: Controlling robots requires deep expertise in ROS2, Gazebo, navigation stacks..." ### Slide 3: The Solution (1:00-1:30) "Gazebo MCP: Natural language → Robot control" ### DEMO 1 (1:30-2:30) - 60 seconds Switch to live demo: [To Claude] "Spawn 3 robots and have them explore this warehouse" [Watch robots appear and start moving] ### Slide 4: Architecture (2:30-3:00) Quick diagram: Claude ↔ MCP Server ↔ Gazebo ### DEMO 2 (3:00-4:00) - 60 seconds [To Claude] "Create an obstacle course and navigate through it" [Watch world generate and robot navigate] ### Slide 5: Call to Action (4:00-5:00) - GitHub link - Documentation - "Try it yourself in 10 minutes" - Q&A ## Backup Plan If demo fails: Pre-recorded 30-second clip If network fails: Local slides only, skip demos ## Audience Engagement Ask at start: "How many of you have programmed a robot?" Ask at end: "Who wants to try this today?" ``` --- #### Script 7.3.2: 15-Minute Technical Deep-Dive ⏳ **Format**: Technical workshop session **Objective**: Educate developers on usage --- #### Script 7.3.3: 30-Minute Full Demonstration ⏳ **Format**: Keynote or main session **Objective**: Comprehensive capability showcase --- #### Script 7.3.4: 1-Hour Hands-On Workshop ⏳ **Format**: Interactive workshop **Objective**: Teach attendees to use the system **Structure**: ```markdown ## Workshop Schedule (60 minutes) ### Part 1: Setup (0:00-0:15) - 15 min - Pre-check: Everyone has laptops ready? - Quick install (if needed) or connect to provided VMs - Verify everyone can start Gazebo ### Part 2: Follow-Along Demo (0:15-0:30) - 15 min [Instructor demonstrates, students follow] 1. Spawn first robot 2. Basic movement commands 3. Read sensor data ### Part 3: Guided Exercises (0:30-0:45) - 15 min Students work on: - Exercise 1: Navigate a square - Exercise 2: Avoid obstacles - Exercise 3: Multi-robot formation Instructors circulate to help ### Part 4: Challenge (0:45-0:55) - 10 min "Create the most interesting robot behavior you can!" ### Part 5: Show & Tell (0:55-1:00) - 5 min 2-3 students show their creations ## Materials Provided - VM images with everything pre-installed - Exercise worksheets - Cheat sheet of common commands - Helper scripts ``` --- #### Script 7.3.5: Investor/Executive Demo ⏳ **Format**: Sales presentation **Duration**: 10 minutes **Objective**: Show business value and ROI --- #### Script 7.3.6: Research Seminar ⏳ **Format**: Academic presentation **Duration**: 45 minutes **Objective**: Demonstrate research applications --- ## Module 7.4: Showcase Applications **Goal**: Build polished applications demonstrating real-world use cases ### Tasks (0/5) #### App 7.4.1: Warehouse Automation Demo ⏳ **Scenario**: Multi-robot warehouse with pick-and-place tasks **Features**: - Realistic warehouse environment - Fleet of delivery robots - Dynamic task allocation - Performance metrics dashboard - Live visualization of robot states **Files**: ``` showcase_apps/warehouse/ ├── README.md # Setup and usage ├── world/ # Warehouse SDF │ ├── warehouse.world │ └── models/ ├── scripts/ │ ├── launch_demo.sh # One-command launch │ ├── generate_tasks.py # Create pick/place tasks │ └── dashboard.py # Real-time metrics ├── configs/ │ └── robots.yaml # Robot fleet config └── docs/ ├── architecture.md └── screenshots/ ``` **README Example**: ```markdown # Warehouse Automation Demo Demonstrates multi-robot coordination for warehouse logistics. ## Quick Start ```bash cd showcase_apps/warehouse ./scripts/launch_demo.sh ``` This launches: - Gazebo with warehouse world - 5 delivery robots - Task generator - Metrics dashboard ## What It Shows - Multi-robot task allocation - Collision avoidance in tight spaces - Battery management simulation - Performance optimization ## Customization Edit `configs/robots.yaml` to change: - Number of robots - Robot types - Task generation rate - World size ## Metrics Tracked - Tasks completed per hour - Average delivery time - Robot utilization % - Collision events - Energy efficiency ``` --- #### App 7.4.2: Search and Rescue Simulation ⏳ **Scenario**: Robots autonomously explore disaster zone **Features**: - Damaged building environment - Autonomous exploration - Victim detection (using visual markers) - Multi-robot coordination - Real-time mapping --- #### App 7.4.3: Agricultural Monitoring ⏳ **Scenario**: Drones survey crop fields **Features**: - Large outdoor terrain - Multiple drone robots - Coverage path planning - Simulated crop health data - Data collection and analysis --- #### App 7.4.4: Autonomous Delivery Fleet ⏳ **Scenario**: Last-mile delivery robots in urban environment --- #### App 7.4.5: Research Platform Demo ⏳ **Scenario**: Reproducible benchmarking environment **Features**: - Standard benchmark worlds - Automated performance testing - Comparison tools - Results visualization - Export for papers/presentations --- ## Module 7.5: Presentation Materials **Goal**: Create professional presentation assets ### Tasks (0/6) #### Material 7.5.1: Main Presentation Deck ⏳ **Format**: PowerPoint/Keynote **Slides**: 30-40 slides **Structure**: ```markdown ## Slide Deck Outline ### Section 1: Introduction (5 slides) 1. Title slide 2. The robotics complexity problem 3. Our solution: Natural language control 4. Architecture overview 5. Why this matters ### Section 2: Core Capabilities (10 slides) 6. Simulation control 7. Robot spawning and management 8. Sensor access 9. World generation 10. Multi-robot coordination 11. Path planning 12. Real-time updates 13. Benchmarking 14. Performance metrics 15. Integration options ### Section 3: Live Demos (5 slides) 16. Demo 1: Hello World 17. Demo 2: Obstacle Course 18. Demo 3: Multi-Robot 19. Demo 4: World Generation 20. Demo 5: Full Workflow ### Section 4: Technical Details (8 slides) 21. MCP Protocol 22. ROS2 Integration 23. Token Efficiency (98.7% savings) 24. Performance benchmarks 25. Supported platforms 26. Extensibility 27. Security 28. Production readiness ### Section 5: Use Cases (5 slides) 29. Research applications 30. Education and training 31. Industrial automation 32. Rapid prototyping 33. Algorithm testing ### Section 6: Getting Started (5 slides) 34. Installation 35. Quick start guide 36. Resources 37. Community 38. Roadmap ### Section 7: Conclusion (2 slides) 39. Summary 40. Call to action ## Speaker Notes Each slide includes detailed speaker notes with: - Key talking points - Timing guidance - Demo triggers - Audience engagement prompts ``` **Files**: - `presentations/main_deck.pptx` - `presentations/main_deck_16x9.key` - `presentations/speaker_notes.pdf` - `presentations/handout.pdf` --- #### Material 7.5.2: Demo Videos ⏳ **Format**: HD video files **Count**: 8 videos (one per core demo) **Specifications**: - Resolution: 1920x1080 minimum - Format: MP4 (H.264) - Duration: 2-5 minutes each - Captions: English subtitles - Audio: Clear narration **Video 1 Example**: ```markdown # Video: "Hello World - Your First AI-Controlled Robot" ## Script [0:00-0:10] Opening "In this video, you'll see how easy it is to control a robot using natural language with the Gazebo MCP Server." [0:10-0:20] Setup [Screen: Terminal and Gazebo side-by-side] "I have Gazebo running on the left, and Claude Code on the right." [0:20-0:40] Spawn Robot [Type to Claude] "Please spawn a TurtleBot3 robot" [Robot appears in Gazebo] "Just like that, we have a robot in our simulation." [0:40-1:00] Movement [Type to Claude] "Make the robot drive forward 2 meters" [Robot moves] "The AI understands the command and executes it." [1:00-1:30] Obstacle Avoidance [Type to Claude] "Add some obstacles and navigate around them" [Obstacles appear, robot navigates] [1:30-2:00] Conclusion "No code written, no ROS2 commands, just natural language. That's the power of MCP for robotics." [End screen: Link to documentation] ## Production Notes - Use screen recording with Kazam or OBS - Ensure terminal font is large (16pt+) - Smooth mouse movements - Clear, enthusiastic narration - Background music: Optional, subtle ``` **Files**: - `videos/01_hello_world.mp4` - `videos/02_obstacle_course.mp4` - `videos/03_multi_robot.mp4` - ... (8 total) - `videos/production_notes.md` - `videos/scripts/` (narration scripts) --- #### Material 7.5.3: Quick Reference Cards ⏳ **Format**: PDF, printable **Size**: 2-page fold-out **Content**: ```markdown # Gazebo MCP Quick Reference ## Front Page ### Getting Started ```bash # Start Gazebo gz sim # Start MCP Server gazebo-mcp-server ``` ### Common Commands (via Claude) **Spawn Robots:** - "Spawn a TurtleBot3 Burger at x=0, y=0" - "Spawn 5 robots in a line" **Movement:** - "Drive forward 2 meters" - "Turn 90 degrees right" - "Navigate to position (5, 3)" **Sensors:** - "Get the latest LiDAR scan" - "Show me camera feed" - "Read IMU data" **World:** - "Create an obstacle course" - "Add a box at position (1, 1)" - "Set lighting to sunset" ## Back Page ### Architecture Diagram [Simple flowchart] Claude ↔ MCP Server ↔ ROS2 ↔ Gazebo ### Troubleshooting **Server won't start:** ☐ ROS2 sourced? ☐ Gazebo running? ☐ Python dependencies installed? **Robot doesn't move:** ☐ Check Gazebo physics is running ☐ Verify robot name is correct ☐ Check for error messages ### Resources - Docs: gazebo-mcp.readthedocs.io - GitHub: github.com/user/gazebo-mcp - Discord: discord.gg/gazebo-mcp - Tutorials: gazebo-mcp.io/tutorials ``` --- #### Material 7.5.4: Promotional Graphics ⏳ **Assets**: - Logo variations (SVG, PNG) - Social media graphics - Banner images - Icon set - Screenshots --- #### Material 7.5.5: One-Page Sell Sheet ⏳ **Format**: PDF, designed for print/email **Content**: ```markdown # Gazebo MCP: AI-Powered Robot Control ## What It Is Control ROS2 robots in Gazebo simulation using natural language through Claude or other AI assistants. No coding required. ## Key Benefits ✓ 98.7% reduction in token usage vs traditional approaches ✓ Natural language robot control ✓ Multi-robot coordination ✓ Production-ready with enterprise features ✓ Open source and extensible ## Use Cases 🔬 Research: Reproducible experiments and benchmarking 🎓 Education: Learn robotics without programming barriers 🏭 Industry: Rapid prototyping and algorithm testing 🤖 Development: Build robot applications faster ## Technical Highlights - Supports ROS2 Humble, Iron, Jazzy - Gazebo Harmonic and Garden - TurtleBot3 robot models included - Full sensor suite (Camera, LiDAR, IMU, GPS) - Procedural world generation - Real-time simulation control ## Get Started in 10 Minutes ```bash git clone [repo] pip install -e . gazebo-mcp-server ``` ## Performance | Metric | Value | |--------|-------| | Spawn latency | <500ms | | Sensor read | <50ms | | Token efficiency | 98.7% | | Concurrent robots | 50+ | ## Support 📖 Documentation: gazebo-mcp.readthedocs.io 💬 Community: Discord, GitHub Discussions 🐛 Issues: GitHub Issues ✉️ Contact: hello@gazebo-mcp.io [QR Code to documentation] ``` --- #### Material 7.5.6: Interactive Demo Sandbox ⏳ **Format**: Web-based demo environment **Features**: - Browser-accessible demo - No installation required - Pre-loaded scenarios - Share-able links - Limited time sessions --- ## Success Criteria ### Module 7.1: Core Demos ✅ - [ ] All 8 demos tested and timed - [ ] Backup plans exist for each demo - [ ] Scripts are clear and easy to follow - [ ] Demos consistently succeed (>90% success rate) - [ ] "Wow moments" identified and reliable ### Module 7.2: Tutorials ✅ - [ ] All 10 tutorials published - [ ] Each tutorial tested with fresh install - [ ] Progressive difficulty validated - [ ] Sample code runs without errors - [ ] Troubleshooting sections complete ### Module 7.3: Live Demo Scripts ✅ - [ ] All 6 scripts created with timing - [ ] Dry runs completed for each script - [ ] Backup plans documented - [ ] Equipment checklists created - [ ] Audience engagement points identified ### Module 7.4: Showcase Apps ✅ - [ ] All 5 apps fully functional - [ ] One-command launch works - [ ] Documentation complete - [ ] Screenshots/videos captured - [ ] Performance metrics validated ### Module 7.5: Presentation Materials ✅ - [ ] Main deck completed and reviewed - [ ] All demo videos produced - [ ] Quick reference cards printed - [ ] Graphics assets created - [ ] One-pager finalized --- ## Testing and Validation ### Demo Rehearsal Checklist For each demo, verify: - [ ] Clean install from scratch works - [ ] All files and configs present - [ ] Timing is accurate (±10%) - [ ] Backup plan tested - [ ] Works on target hardware - [ ] Network requirements documented - [ ] Recovery procedures work ### Audience Testing Conduct demos for: - [ ] Technical audience (developers) - [ ] Non-technical audience (executives) - [ ] Students (education) - [ ] Researchers (academic) - [ ] Industry practitioners Collect feedback on: - Clarity of presentation - Pacing and timing - Technical depth - Engagement level - Questions raised --- ## Production Guidelines ### Video Production **Equipment**: - Screen recorder: OBS Studio or Kazam - Microphone: USB condenser mic (minimum) - Editing: DaVinci Resolve or Adobe Premiere **Quality Standards**: - Minimum 1080p resolution - Clear audio (no background noise) - Consistent branding - Professional transitions - Accurate captions ### Presentation Design **Visual Guidelines**: - Consistent color scheme - Large, readable fonts (24pt minimum) - High contrast for projectors - Minimal text per slide - Professional graphics - Dark background for demos --- ## Distribution Channels ### Where to Share **Documentation**: - ReadTheDocs site - GitHub Wiki - Project website **Videos**: - YouTube channel - Vimeo for embeds - Social media clips **Demos**: - Conference presentations - Webinars - Meetup groups - University lectures **Tutorials**: - Medium articles - Dev.to posts - Personal blogs - Online courses (Udemy, Coursera) --- ## Maintenance ### Keeping Demos Current Quarterly review: - [ ] Test all demos with latest version - [ ] Update screenshots if UI changed - [ ] Refresh timing estimates - [ ] Check external links - [ ] Update dependency versions - [ ] Re-record outdated videos --- ## Next Phase Once all demonstrations are complete, proceed to: **Phase 8: Community Building & Growth** (if planned) Or return to: **Phase 5: Optional Enhancements** - to add advanced features **Phase 6: Testing & Documentation** - for comprehensive testing --- **Estimated Completion**: 2-3 weeks **Priority**: MEDIUM-HIGH (valuable for adoption and education) **Status**: 🔵 Not Started **Note:** This phase significantly improves project visibility, adoption, and user success. While optional, it's highly recommended for projects seeking wider adoption.