Skip to main content

Learning Outcomes

By the end of this course, you will be able to:

1. Design and Deploy ROS 2 Systems​

Build distributed robotic systems using ROS 2 nodes, topics, services, and actions for real-time control of humanoid robots.

Skills Acquired:

  • Create publishers and subscribers for sensor data streaming
  • Implement services for request-response patterns (path planning, IK)
  • Use actions for long-running tasks (navigation, grasping)
  • Debug distributed systems with ROS 2 CLI tools

Assessments: Project 1 (ROS 2 Architecture), Weekly Labs 1-3

2. Create Digital Twin Simulations​

Develop high-fidelity physics simulations in Gazebo and Unity for sensor testing, motion planning, and visualization.

Skills Acquired:

  • Configure physics engines (ODE, DART) for stable humanoid simulation
  • Integrate virtual sensors (LiDAR, depth cameras, IMU) with realistic noise models
  • Build Gazebo worlds with obstacles and dynamic objects
  • Render photorealistic scenes in Unity with HDRP

Assessments: Project 2 (Gazebo Humanoid Simulation), Weekly Labs 4-6

3. Integrate AI-Powered Navigation​

Implement NVIDIA Isaac ROS modules for VSLAM, synthetic data generation, and bipedal navigation using Nav2.

Skills Acquired:

  • Generate synthetic training datasets with Isaac Sim and Replicator
  • Deploy GPU-accelerated visual SLAM on NVIDIA Jetson hardware
  • Adapt Nav2 costmaps and planners for humanoid footstep planning
  • Implement Zero Moment Point (ZMP) stability checks

Assessments: Project 3 (Isaac VSLAM Integration), Weekly Labs 7-10

4. Build Vision-Language-Action Systems​

Combine voice input (Whisper), LLM reasoning (GPT-4), and robotic action execution for autonomous task planning.

Skills Acquired:

  • Integrate OpenAI Whisper for robust speech-to-text
  • Use LLMs for task decomposition and error recovery
  • Implement ReAct (Reasoning + Acting) patterns
  • Build end-to-end voice-controlled autonomous systems

Assessments: Capstone Project (Autonomous Humanoid), Weekly Labs 11-13

5. Optimize Robot Perception Systems​

Configure and tune LiDAR, depth cameras, and IMU sensors for reliable environmental perception in dynamic settings.

Skills Acquired:

  • Calibrate sensor intrinsics and extrinsics
  • Implement sensor fusion (LiDAR + IMU + vision)
  • Handle sensor noise, occlusions, and environmental variations
  • Optimize perception pipelines for real-time performance (<50ms latency)

Assessments: All hands-on labs, sensor calibration assignments

6. Deploy Production-Ready Robotics Software​

Containerize, version, and deploy robotic applications using Docker, Git, and CI/CD best practices.

Skills Acquired:

  • Structure ROS 2 packages following best practices
  • Use Git for version control and GitHub Actions for CI/CD
  • Write unit tests and integration tests for robot nodes
  • Document code with clear README, API docs, and architecture diagrams

Assessments: Code reviews, deployment assignments, capstone submission

Cross-Cutting Skills​

Beyond technical competencies, this course develops:

  • Systems Thinking: Understanding how sensors, planning, and control interact
  • Debugging Resilience: Systematically diagnosing failures in distributed systems
  • Rapid Prototyping: Iterating quickly from simulation to real hardware
  • Documentation: Communicating technical designs clearly to stakeholders

Certification Requirements​

To successfully complete this course:

  1. ✅ Complete all 4 module projects with passing scores (70%+)
  2. ✅ Submit capstone project demonstrating end-to-end integration
  3. ✅ Pass 10/13 weekly labs (drop lowest 3 scores)
  4. ✅ Achieve 90+ scores on Lighthouse performance/accessibility audits (for book project)

View detailed weekly breakdown →

View all assessments →

Return to Homepage →