Robotics with Physical AI: A to Z in Simulation-Based Robotics

• 1.1. Robotics: Definition, History, and Types of Robots
• 1.2. Robot Anatomy — Links, Joints, and Degrees of Freedom
• 1.3. Coordinate Frames and Transformations
• 1.4. Forward & Inverse Kinematics Basics
• 1.5. Real-World Applications of Robotics Across Industries

Understand the fundamental concepts of robotics, robot anatomy, and the kinematics that govern how robots move.

To gain a comprehensive overview of robotics and understand how physical AI systems are structured before diving into simulation.

• 2.1. Introduction to ROS 2 & Installation & Setup
• 2.2. Nodes, Topics, and the Publish-Subscribe Model
• 2.3. Services & Actions in ROS 2
• 2.4. Writing ROS 2 Packages & Launch Files
• 2.5. URDF — Describing Robots for ROS 2
• 2.6. Using rviz2 for Visualization
• 2.7. ROS 2 Command-Line Tools & Debugging

Master the ROS 2 framework — building, connecting, and debugging the software components that drive real robots.

ROS 2 is the industry-standard framework used to build almost every modern robotics system, from research labs to Amazon warehouses.

• 3.1. Introduction to Robot Sensors and Use Cases
• 3.2. LiDAR, Depth Cameras & IMU Sensors
• 3.3. Actuators — Servo Motors, Stepper Motors & Grippers
• 3.4. Sensor Fusion Basics
• 3.5. Reading & Publishing Sensor Data in ROS 2
• 3.6. Handling Sensor Noise & Calibration
• 3.7. Building a Basic Perception Pipeline

Learn how robots sense their environment and act on it — from raw sensor data to real actuator commands.

Sensors and actuators are the eyes, ears, and muscles of any robot — understanding them is essential to building anything that interacts with the physical world.

• 4.1. What is Physical AI? From Chatbots to Embodied Agents
• 4.2. Embodied Intelligence — How Robots Learn from Interaction
• 4.3. Reinforcement Learning Basics for Robotics
• 4.4. Sim-to-Real Transfer Concepts
• 4.5. Foundation Models for Robotics (Overview)
• 4.6. Ethical & Safety Considerations in Physical AI

Gain a strong conceptual foundation in Physical AI — how intelligence embodied in a robot differs from software-only AI.

Physical AI is the fastest-growing frontier of AI, powering humanoid robots, self-driving cars, and autonomous warehouses.

• 5.1. Introduction to Gazebo & Installation
• 5.2. Building Worlds — Terrain, Objects & Physics
• 5.3. Spawning URDF/SDF Robot Models in Gazebo
• 5.4. Simulating Sensors (Camera, LiDAR, IMU) in Gazebo
• 5.5. Connecting Gazebo to ROS 2 (ros_gz Bridge)
• 5.6. Testing Robot Behaviour in a Virtual Environment

Master robot simulation using Gazebo, focusing on world-building, sensor simulation, and testing robot behaviour safely in software.

Gazebo lets you build and test an entire robot — with realistic physics and sensors — without ever touching physical hardware.

• 6.1. Introduction to NVIDIA Isaac Sim & Omniverse
• 6.2. Photorealistic Environments & Physics Simulation
• 6.3. Importing & Configuring Robot Models
• 6.4. Building Digital Twins of Real-World Environments
• 6.5. Synthetic Data Generation for AI Training
• 6.6. Connecting Isaac Sim to ROS 2

Learn to build high-fidelity digital twins and generate synthetic training data using NVIDIA's Isaac Sim platform.

Isaac Sim is the industry-leading simulator used by companies like NVIDIA, Figure and Boston Dynamics to train robots entirely in simulation.

• 7.1. Introduction to Computer Vision for Robots
• 7.2. Image Processing Basics with OpenCV
• 7.3. Object Detection & Recognition
• 7.4. Depth Estimation & Point Clouds
• 7.5. Visual SLAM Concepts
• 7.6. Integrating Vision Models with ROS 2

Apply computer vision techniques so simulated robots can see, detect objects, and understand their surroundings.

Vision is the primary sense for most modern robots — from warehouse pick-and-place arms to self-driving cars.

• 8.1. Path Planning Algorithms (A*, RRT)
• 8.2. Navigation Stack (Nav2) Overview
• 8.3. PID Control for Robot Motion
• 8.4. AI-Based Trajectory Optimization
• 8.5. Obstacle Avoidance in Dynamic Environments
• 8.6. Testing Motion Planners in Simulation

Design intelligent motion planning and control systems that let a simulated robot navigate autonomously.

Motion planning is what turns a robot from a static machine into an autonomous agent capable of navigating real-world spaces safely.

• Live project implementation and hands-on simulation experience
• Warehouse automation robot case study — perception to pick-and-place
• Humanoid navigation case study — sensing, planning & control
• Creating a professional portfolio of simulated robot projects
• Final assessment & Robotics with Physical AI Certification

By the end of the internship, you will have built a complete simulated robot — from sensors to motion — to showcase to recruiters and earn your recognized certification.