Assessments

This appendix provides an overview of the main assessments throughout the book, along with rubrics and success checklists for each.

Assessment Philosophy

Assessments in this book are designed to be:

• Practical: Hands-on projects that mirror real-world robotics development
• Progressive: Each assessment builds on previous concepts
• Flexible: Can be adapted to different skill levels and available hardware
• Authentic: Reflect actual challenges in Physical AI development

Module 1 Assessment: ROS 2 Package Project

Objective

Create a ROS 2 package that demonstrates understanding of nodes, topics, services, and launch files.

Requirements

Minimum Requirements:

• Package builds successfully with colcon build
• Contains at least one publisher node
• Contains at least one subscriber node
• Contains at least one service (client or server)
• Contains a launch file that starts all nodes
• README.md explains package structure and usage

Success Criteria:

• All nodes communicate properly via topics/services
• Code follows ROS 2 best practices (logging, error handling)
• Launch file works without errors
• Documentation is clear and complete

Rubric

Criteria	Excellent (4)	Good (3)	Satisfactory (2)	Needs Improvement (1)
Functionality	All features work perfectly	Most features work, minor issues	Basic features work	Significant functionality missing
Code Quality	Clean, well-organized, follows best practices	Mostly clean, minor issues	Functional but messy	Poor organization, many issues
Documentation	Excellent, comprehensive	Good, covers essentials	Basic, missing some details	Incomplete or unclear
Innovation	Goes beyond requirements	Meets all requirements	Meets most requirements	Fails to meet requirements

Stretch Goals

• Implement an action server/client
• Add parameter configuration
• Create custom message types
• Add unit tests

Module 2 Assessment: Gazebo Simulation Project

Objective

Create a robot model in Gazebo and demonstrate control via ROS 2.

Requirements

Minimum Requirements:

• URDF/SDF model of a robot (mobile base or simple arm)
• Robot spawns correctly in Gazebo
• At least one sensor (camera or LiDAR) publishes to ROS 2 topics
• At least one joint can be controlled via ROS 2 topics or ros2_control
• Launch file spawns robot and sensors
• Documentation with screenshots

Success Criteria:

• Robot model is physically realistic (proper mass, inertia)
• Sensor data is available on ROS 2 topics
• Joint control works reliably
• Model is reusable and well-documented

Rubric

Criteria	Excellent (4)	Good (3)	Satisfactory (2)	Needs Improvement (1)
Model Quality	Realistic physics, well-designed	Good physics, minor issues	Basic model, some issues	Unrealistic or broken
Sensor Integration	Sensors work perfectly, good data	Sensors work, minor issues	Basic sensor functionality	Sensors don't work
Control	Smooth, reliable control	Mostly reliable	Basic control works	Control doesn't work
Documentation	Excellent with clear visuals	Good documentation	Basic documentation	Poor documentation

Stretch Goals

• Multiple sensors (camera + LiDAR + IMU)
• Complex robot model (humanoid or multi-DOF arm)
• Integration with Nav2 or MoveIt
• Custom Gazebo plugins

Module 3 Assessment: Isaac ROS Pipeline Project

Objective

Set up an Isaac ROS perception pipeline and verify data flow.

Requirements

Minimum Requirements:

• Isaac Sim launches with robot model and sensors
• At least one Isaac ROS perception pipeline configured (VSLAM or object detection)
• Pipeline processes sensor data and publishes to ROS 2 topics
• Data can be visualized (RViz or similar)
• Documentation with pipeline diagram

Success Criteria:

• Isaac Sim environment is stable and functional
• Perception pipeline processes data correctly
• ROS 2 topics contain valid perception data
• Pipeline is documented and reproducible

Rubric

Criteria	Excellent (4)	Good (3)	Satisfactory (2)	Needs Improvement (1)
Setup	Perfect setup, no issues	Minor setup issues resolved	Setup works with help	Setup doesn't work
Pipeline	Pipeline works perfectly	Pipeline works, minor issues	Basic pipeline works	Pipeline doesn't work
Data Quality	High-quality perception data	Good data quality	Acceptable data	Poor or no data
Documentation	Excellent with diagrams	Good documentation	Basic documentation	Poor documentation

Stretch Goals

• Multiple perception pipelines (VSLAM + object detection)
• Custom model training
• Integration with navigation stack
• Performance optimization

Module 4 Assessment: VLA Pipeline Project

Objective

Build a minimal VLA system that processes natural language and executes ROS 2 actions.

Requirements

Minimum Requirements:

• Accepts voice or text input
• Transcribes to text (Whisper or similar)
• LLM generates structured task plans from commands
• At least one ROS 2 action executes based on plan
• System handles basic error cases
• Documentation with example inputs/outputs

Success Criteria:

• System processes natural language commands
• LLM generates reasonable task plans
• ROS 2 actions execute correctly
• Error handling prevents crashes

Rubric

Criteria	Excellent (4)	Good (3)	Satisfactory (2)	Needs Improvement (1)
Language Understanding	Excellent command parsing	Good understanding	Basic understanding	Poor understanding
Task Planning	High-quality plans	Good plans	Acceptable plans	Poor or no plans
Action Execution	All actions work perfectly	Most actions work	Basic actions work	Actions don't work
Robustness	Handles errors gracefully	Mostly robust	Basic error handling	Crashes on errors

Stretch Goals

• Multiple action types (navigation + manipulation)
• Error recovery and replanning
• Voice input with real-time processing
• Multi-step task sequences

Capstone Assessment: The Autonomous Humanoid

Objective

Integrate all modules into a complete autonomous humanoid system.

Requirements

Minimum Requirements:

• Voice/text input → task planning pipeline works
• At least 2 different ROS 2 action types execute
• Robot performs behaviors in simulation (Gazebo or Isaac Sim)
• System handles at least 3 different command types
• Architecture diagram and documentation
• Demonstration video or live demo

Success Criteria:

• Complete voice-to-action pipeline functions
• Multiple action types work correctly
• Simulation demonstrates robot behaviors
• System is documented and reproducible

Rubric

Criteria	Excellent (4)	Good (3)	Satisfactory (2)	Needs Improvement (1)
Integration	All modules integrated perfectly	Most modules integrated	Basic integration	Poor integration
Functionality	All features work excellently	Most features work	Basic features work	Significant gaps
Complexity	Handles complex scenarios	Handles moderate scenarios	Handles simple scenarios	Fails on basic scenarios
Documentation	Excellent, comprehensive	Good documentation	Basic documentation	Poor documentation
Innovation	Creative solutions, goes beyond	Meets all requirements	Meets most requirements	Fails requirements

Stretch Goals

• Advanced perception (object detection, semantic segmentation)
• Complex manipulation sequences
• Error recovery and replanning
• Real robot deployment
• Multi-robot coordination

General Assessment Tips

Before Starting

• Read the requirements carefully
• Understand the success criteria
• Plan your approach
• Set up your development environment

During Development

• Test incrementally
• Document as you go
• Use version control (Git)
• Ask for help when stuck

Before Submission

• Test all functionality
• Review documentation
• Check code quality
• Verify all requirements are met

Getting Help

• Review module materials
• Check official documentation
• Ask on forums (ROS Discourse, etc.)
• Consult with instructors/peers

Assessment Submission Checklist

For each assessment, ensure you have:

• All required files and code
• README.md with setup instructions
• Documentation explaining your approach
• Screenshots or videos demonstrating functionality
• Clear explanation of any limitations or known issues
• References to any external resources used

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Use these rubrics as guidelines. Instructors may adapt them based on specific course requirements and available resources.