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NVIDIA Isaac Sim. Total Immersion

NVIDIA Isaac Sim. Total Immersion

Curriculum

30 Sections
284 Lessons
Lifetime

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0QE8 0QE8 1. Introduction to Robotic Simulations
6
- 1.1
  0QE8 1.1 What Are Robotic Simulations and Why They Are Needed
- 1.2
  0QE8 1.2 Overview of Existing Simulators — Gazebo, Webots, PyBullet, Isaac Sim
- 1.3
  0QE8 1.3 Advantages of NVIDIA Isaac Sim — Photorealism, PhysX, GPU Acceleration
- 1.4
  0QE8 1.4 NVIDIA Ecosystem — Isaac Sim, Isaac Lab, GROOT, ROS 2
- 1.5
  0QE8 1.5 Applications of Simulation — Robot Training, Testing, Prototyping
- 1.6
  0QE8 1.6 Sim-to-Real Transfer — Transitioning from Simulation to Reality
0QE8 0QE8 2. Installation and Setup
10
- 2.1
  0QE8 2.1 System Requirements — Hardware, Drivers, Operating Systems
- 2.2
  0QE8 2.2 Installing NVIDIA Drivers and CUDA Toolkit
- 2.3
  0QE8 2.3 Installing Omniverse Launcher — Interface Overview
- 2.4
  0QE8 2.4 Installing Isaac Sim via Omniverse Launcher
- 2.5
  0QE8 2.5 Alternative Installation Methods — Docker, Native, Cloud
- 2.6
  0QE8 2.6 Installing Omniverse Nucleus for Local Asset Storage
- 2.7
  0QE8 2.7 Installing Omniverse Cache for Asset Acceleration
- 2.8
  0QE8 2.8 First Isaac Sim Launch — Startup Process
- 2.9
  0QE8 2.9 Setting Up Python Environment for the API
- 2.10
  0QE8 2.10 Troubleshooting Common Installation Issues
0QE8 0QE8 3. Interface and Basics
12
- 3.1
  0QE8 3.1 Main Window Overview — Viewport, Panels, Menus, Toolbars
- 3.2
  0QE8 3.2 Navigating 3D Space — Camera Movement, Zoom, Rotation
- 3.3
  0QE8 3.3 Stage Concept — Scene and Object Hierarchy
- 3.4
  0QE8 3.4 Creating Your First Scene — Ground Plane and Lighting
- 3.5
  0QE8 3.5 Adding Simple Primitives — Cubes, Spheres, Cylinders
- 3.6
  0QE8 3.6 Object Transformations — Position, Rotation, Scale
- 3.7
  0QE8 3.7 Property Panel — Exploring Object Properties
- 3.8
  0QE8 3.8 Materials and Textures — Visual Diversity
- 3.9
  0QE8 3.9 Lighting System — Light Types and Configuration
- 3.10
  0QE8 3.10 First Simulation Run — Using the Play Button
- 3.11
  0QE8 3.11 Simulation Time Control — Timestep, Speed, Pause
- 3.12
  0QE8 3.12 Saving and Loading Scenes — USD Format
0QE8 0QE8 4. USD Format Essentials
7
- 4.1
  0QE8 4.1 What Is USD and Why It Matters for Robotics
- 4.2
  0QE8 4.2 USD File Structure — Prims, Attributes, Relationships
- 4.3
  0QE8 4.3 USD Layers — Composing Scenes from Multiple Files
- 4.4
  0QE8 4.4 References and Payloads — Asset Reuse Patterns
- 4.5
  0QE8 4.5 Variants — Creating Efficient Object Variations
- 4.6
  0QE8 4.6 Working with USD via the Python API
- 4.7
  0QE8 4.7 Exporting and Importing USD Files
0QE8 0QE8 5. Physics Engine PhysX 5
10
- 5.1
  0QE8 5.1 Introduction to the PhysX 5 Physics Engine
- 5.2
  0QE8 5.2 Rigid Bodies — Mass, Inertia, Center of Mass
- 5.3
  0QE8 5.3 Collision Shapes — Collision Detection Forms
- 5.4
  0QE8 5.4 Visual vs. Collision Meshes — Differences
- 5.5
  0QE8 5.5 Physics Materials — Friction, Elasticity, Damping
- 5.6
  0QE8 5.6 Gravity and Global Forces in Simulation
- 5.7
  0QE8 5.7 Contacts and Collision Handling — Detection and Response
- 5.8
  0QE8 5.8 PhysX Scene Parameters — Accuracy vs. Performance
- 5.9
  0QE8 5.9 Deformable Bodies — Soft Objects and Fluids
- 5.10
  0QE8 5.10 Particle Systems — Granular Materials Simulation
0QE8 0QE8 6. Joints, Articulations, and Mechanisms
10
- 6.1
  0QE8 6.1 Joint Concepts in Robotics — Fundamentals
- 6.2
  0QE8 6.2 Joint Types — Revolute, Prismatic, Fixed, Spherical
- 6.3
  0QE8 6.3 Creating a First Joint — Connecting Two Bodies
- 6.4
  0QE8 6.4 Degrees of Freedom (DoF) Explained
- 6.5
  0QE8 6.5 Articulation Concept — Linked Body Systems
- 6.6
  0QE8 6.6 Kinematic Chains — Mechanics
- 6.7
  0QE8 6.7 Joint Limits — Angle and Position Constraints
- 6.8
  0QE8 6.8 Joint Drives and Actuators — Position, Velocity, Effort
- 6.9
  0QE8 6.9 Stiffness and Damping — Joint Properties
- 6.10
  0QE8 6.10 Creating a Simple Two-Link Manipulator
0QE8 0QE8 7. Importing and Configuring Robots
9
- 7.1
  0QE8 7.1 Isaac Sim Asset Library — Finding Ready Robots
- 7.2
  0QE8 7.2 Importing URDF Files (ROS Robot Description Format)
- 7.3
  0QE8 7.3 Importing MJCF Files (MuJoCo Format)
- 7.4
  0QE8 7.4 Converting Models to USD Format
- 7.5
  0QE8 7.5 Importing Popular Robots — Franka Panda, UR5, Fetch
- 7.6
  0QE8 7.6 Verifying Import Correctness — Joints, Links, Meshes
- 7.7
  0QE8 7.7 Fixing Issues with Imported Models
- 7.8
  0QE8 7.8 Configuring Robot Visualization — Materials and Colors
- 7.9
  0QE8 7.9 Configuring Physical Properties and Parameters
0QE8 0QE8 8. Controllers and Robot Control
11
- 8.1
  0QE8 8.1 Controller Types — Position, Velocity, Effort
- 8.2
  0QE8 8.2 Creating a Simple Position Controller for a Joint
- 8.3
  0QE8 8.3 PID Controllers — Theory and Practical Tuning
- 8.4
  0QE8 8.4 Velocity Control Implementation
- 8.5
  0QE8 8.5 Torque and Force Control Systems
- 8.6
  0QE8 8.6 Impedance Control — Compliant Collaboration
- 8.7
  0QE8 8.7 Inverse Kinematics (IK) — Solving
- 8.8
  0QE8 8.8 Forward Kinematics (FK) — Calculations
- 8.9
  0QE8 8.9 Configuring an IK Controller for a Manipulator
- 8.10
  0QE8 8.10 Trajectory Planning — Path Generation
- 8.11
  0QE8 8.11 Motion Planning Algorithms — RRT and PRM Basics
0QE8 0QE8 9. Sensors and Environmental Perception
12
- 9.1
  0QE8 9.1 Sensor Overview — Vision, Range, Proprioception
- 9.2
  0QE8 9.2 RGB Cameras — Resolution, FOV, Frequency
- 9.3
  0QE8 9.3 Depth Cameras — Obtaining Depth Maps
- 9.4
  0QE8 9.4 Stereo Cameras — 3D Perception
- 9.5
  0QE8 9.5 LiDAR — 2D and 3D Laser Rangefinders
- 9.6
  0QE8 9.6 IMU Sensors — Acceleration and Angular Velocity
- 9.7
  0QE8 9.7 Force/Torque Sensors — Measuring Joint Forces
- 9.8
  0QE8 9.8 Contact Sensors — Detecting Touch and Interaction
- 9.9
  0QE8 9.9 Encoders — Reading Joint Positions
- 9.10
  0QE8 9.10 Getting Sensor Data via the Python API
- 9.11
  0QE8 9.11 Visualizing Sensor Data in Real Time
- 9.12
  0QE8 9.12 Sensor Noise and Domain Randomization for Robustness
0QE8 0QE8 10. Robot Types and Capabilities
9
- 10.1
  0QE8 10.1 Manipulators — Kinematics, Workspace, Singularities
- 10.2
  0QE8 10.2 Configuring Franka Panda for Pick-and-Place
- 10.3
  0QE8 10.3 Mobile Robots — Differential Drive and Holonomic
- 10.4
  0QE8 10.4 Creating a Wheeled Mobile Robot Simulation
- 10.5
  0QE8 10.5 Humanoid Robots — Balance, Locomotion, Control Complexity
- 10.6
  0QE8 10.6 Quadcopters and Drones — Flight Dynamics and Stabilization
- 10.7
  0QE8 10.7 Collaborative Robots (Cobots) — Safety and Compliance
- 10.8
  0QE8 10.8 Hybrid Robots — Mobile Manipulators (Fetch, TIAGo)
- 10.9
  0QE8 10.9 Underwater and Aerial Robots — Special Physics
0QE8 0QE8 11. Python API and Simulation Programming
11
- 11.1
  0QE8 11.1 Introduction to the Isaac Sim Python API
- 11.2
  0QE8 11.2 Script Structure — Setup, Step Loop, Cleanup
- 11.3
  0QE8 11.3 Creating Scenes Programmatically — Adding Objects
- 11.4
  0QE8 11.4 Controlling Robots via Python — Sending Commands
- 11.5
  0QE8 11.5 Reading Sensor Data Programmatically
- 11.6
  0QE8 11.6 Standalone Scripts vs. Extension API
- 11.7
  0QE8 11.7 Creating the First Standalone Script
- 11.8
  0QE8 11.8 Working with SimulationContext for Control
- 11.9
  0QE8 11.9 Callbacks and Events — Reacting to Simulation Events
- 11.10
  0QE8 11.10 Debugging Python Code in Isaac Sim
- 11.11
  0QE8 11.11 Best Practices for Efficient Robotics Code
0QE8 0QE8 12. Isaac Lab RL Framework
13
- 12.1
  0QE8 12.1 What Is Isaac Lab and How It Differs from Isaac Sim
- 12.2
  0QE8 12.2 Installing Isaac Lab — Environment Setup and Configuration
- 12.3
  0QE8 12.3 Isaac Lab Architecture — Managers, Environments, Wrappers
- 12.4
  0QE8 12.4 Ready Environments — Available Tasks Overview
- 12.5
  0QE8 12.5 Running the First RL Environment — CartPole Example
- 12.6
  0QE8 12.6 RL Task Structure — Observations, Actions, Rewards
- 12.7
  0QE8 12.7 Creating a Custom Environment from Scratch
- 12.8
  0QE8 12.8 Observation Manager — Configuring Agent Perception
- 12.9
  0QE8 12.9 Action Manager — Defining Action Space
- 12.10
  0QE8 12.10 Reward Manager — Reward Function Design
- 12.11
  0QE8 12.11 Termination Conditions — Episode End Criteria
- 12.12
  0QE8 12.12 Parallel Environments — Running Thousands of Robots
- 12.13
  0QE8 12.13 Domain Randomization — Variability for Robustness
0QE8 0QE8 13. Reinforcement Learning Training
11
- 13.1
  0QE8 13.1 RL Basics — Agent, Environment, Policy
- 13.2
  0QE8 13.2 Popular RL Algorithms — PPO, SAC, DQN
- 13.3
  0QE8 13.3 Integration with RL Libraries — SB3 and RL Games
- 13.4
  0QE8 13.4 Hyperparameter Tuning for Training
- 13.5
  0QE8 13.5 Starting the Training Loop
- 13.6
  0QE8 13.6 Monitoring with TensorBoard and Weights & Biases
- 13.7
  0QE8 13.7 Policy Evaluation and Performance Testing
- 13.8
  0QE8 13.8 Saving and Loading Trained Policies (Checkpoints)
- 13.9
  0QE8 13.9 Fine-Tuning Pretrained Policies
- 13.10
  0QE8 13.10 Sim-to-Real Transfer of Policies
- 13.11
  0QE8 13.11 Troubleshooting Training Instability and Low Reward
0QE8 0QE8 14. Imitation Learning and Demonstrations
10
- 14.1
  0QE8 14.1 What Is Imitation Learning and When to Use It
- 14.2
  0QE8 14.2 Teleoperation — Human-in-the-Loop Robot Control
- 14.3
  0QE8 14.3 Recording Demonstrations in Isaac Sim
- 14.4
  0QE8 14.4 Demonstration Data Format and HDF5 Structure
- 14.5
  0QE8 14.5 Isaac Lab Mimic — Automatic Demonstration Generation
- 14.6
  0QE8 14.6 Annotating Subtasks in Demonstrations
- 14.7
  0QE8 14.7 Behavioral Cloning from Imitation Data
- 14.8
  0QE8 14.8 Training Policies on Collected Datasets
- 14.9
  0QE8 14.9 Dataset Aggregation (DAgger) — Iterative Improvement
- 14.10
  0QE8 14.10 Comparing IL and RL — When to Use Which
0QE8 0QE8 15. Vision-Language Models (VLM)
8
- 15.1
  0QE8 15.1 Introduction to Vision-Language Model Capabilities
- 15.2
  0QE8 15.2 Popular VLMs — CLIP, BLIP, LLaVA, PaliGemma
- 15.3
  0QE8 15.3 Integrating a VLM with Isaac Sim via Python
- 15.4
  0QE8 15.4 Visual Grounding — Linking Language to Objects
- 15.5
  0QE8 15.5 Processing Camera Images for VLM Input
- 15.6
  0QE8 15.6 Zero-Shot Object Detection with VLMs
- 15.7
  0QE8 15.7 Natural Language Command Systems for Robots
- 15.8
  0QE8 15.8 Using a VLM as a High-Level Planner
0QE8 0QE8 16. Vision-Language-Action Models (VLA)
10
- 16.1
  0QE8 16.1 What Are VLA Models — From Perception to Actions
- 16.2
  0QE8 16.2 VLA Architecture — Vision Encoder, Language Module, Action Head
- 16.3
  0QE8 16.3 Popular VLA Models — RT-2, OpenVLA, SmolVLA, Octo
- 16.4
  0QE8 16.4 Preparing Data for VLA Training — Datasets
- 16.5
  0QE8 16.5 Action Tokenization — Representing Actions
- 16.6
  0QE8 16.6 Fine-Tuning a VLA Model on a Custom Task
- 16.7
  0QE8 16.7 VLA Inference in Isaac Sim — Real-Time Actions
- 16.8
  0QE8 16.8 Action Chunking — Predicting Sequences
- 16.9
  0QE8 16.9 Asynchronous Inference for Better Reactivity
- 16.10
  0QE8 16.10 Comparing VLA with Classic RL Approaches
0QE8 0QE8 17. Large Language Models as Planners
8
- 17.1
  0QE8 17.1 LLMs in Robotics — High-Level Planning
- 17.2
  0QE8 17.2 Integrating OpenAI APIs and Local LLMs with Isaac Sim
- 17.3
  0QE8 17.3 Prompt Engineering for Robotics Tasks
- 17.4
  0QE8 17.4 Decomposing Complex Tasks into Subtasks with LLMs
- 17.5
  0QE8 17.5 Function Calling — Executing Robot Commands via LLM
- 17.6
  0QE8 17.6 Reasoning for Complex Manipulation
- 17.7
  0QE8 17.7 Multimodal LLMs — Processing Images and Text
- 17.8
  0QE8 17.8 Error Recovery and Safety Mechanisms with LLMs
0QE8 0QE8 18. Isaac GROOT Generalist Policies
10
- 18.1
  0QE8 18.1 Introduction to Isaac GROOT Generalist Robot Technology
- 18.2
  0QE8 18.2 GROOT Architecture — Foundation Model for Robots
- 18.3
  0QE8 18.3 GR00T-N1 Model — Capabilities and Limitations
- 18.4
  0QE8 18.4 Integrating GROOT with Isaac Lab
- 18.5
  0QE8 18.5 Loading Pretrained GROOT Policies
- 18.6
  0QE8 18.6 Evaluating GROOT in Simulation
- 18.7
  0QE8 18.7 Fine-Tuning GROOT for Specific Tasks
- 18.8
  0QE8 18.8 Data Collection for GROOT — Dataset Requirements
- 18.9
  0QE8 18.9 GROOT for Humanoid Locomotion Control
- 18.10
  0QE8 18.10 Multi-Embodiment Learning — Training Across Robots
0QE8 0QE8 19. ROS 2 Integration Ecosystem
11
- 19.1
  0QE8 19.1 Introduction to ROS 2 — Core Concepts
- 19.2
  0QE8 19.2 Installing ROS 2 and Setup with Isaac Sim
- 19.3
  0QE8 19.3 Isaac Sim ROS 2 Bridge — Installation and Configuration
- 19.4
  0QE8 19.4 Publishing Sensor Data to ROS Topics
- 19.5
  0QE8 19.5 Subscribing to Control Commands from ROS Topics
- 19.6
  0QE8 19.6 TF Transforms — Building the Transform Tree
- 19.7
  0QE8 19.7 Visualizing Isaac Sim Data in RViz
- 19.8
  0QE8 19.8 Using Navigation2 with Isaac Sim
- 19.9
  0QE8 19.9 MoveIt2 for Manipulator Motion Planning
- 19.10
  0QE8 19.10 Creating Custom ROS 2 Nodes for Control
- 19.11
  0QE8 19.11 ROS 2 Actions for Long-Running Tasks
0QE8 0QE8 20. Navigation and SLAM
10
- 20.1
  0QE8 20.1 Autonomous Navigation Basics
- 20.2
  0QE8 20.2 Configuring a Mobile Robot with LiDAR
- 20.3
  0QE8 20.3 Obstacle Avoidance and Collision Prevention
- 20.4
  0QE8 20.4 Path Planning — A* and RRT
- 20.5
  0QE8 20.5 SLAM — Simultaneous Localization and Mapping Basics
- 20.6
  0QE8 20.6 Creating an Environment Map in Simulation
- 20.7
  0QE8 20.7 Robot Localization on a Map
- 20.8
  0QE8 20.8 Navigation Stack — Autonomous Driving in Isaac Sim
- 20.9
  0QE8 20.9 Waypoint Navigation Through Multiple Goals
- 20.10
  0QE8 20.10 Dynamic Obstacles — Responding to Moving Objects
0QE8 0QE8 21. Manipulation and Pick-Place
12
- 21.1
  0QE8 21.1 Robotic Manipulation Basics
- 21.2
  0QE8 21.2 Gripper Types — Parallel, Vacuum, Dexterous
- 21.3
  0QE8 21.3 Grasp Planning — Approach Strategies
- 21.4
  0QE8 21.4 Configuring Controllers for Gripper Control
- 21.5
  0QE8 21.5 Building a Pick-and-Place Scene
- 21.6
  0QE8 21.6 Object Pose Estimation
- 21.7
  0QE8 21.7 Approach Trajectory Design
- 21.8
  0QE8 21.8 Implementing Grasp Mechanics
- 21.9
  0QE8 21.9 Lifting and Transport Operations
- 21.10
  0QE8 21.10 Precise Placement and Alignment
- 21.11
  0QE8 21.11 Error Handling and Recovery
- 21.12
  0QE8 21.12 Multi-Object Manipulation
0QE8 0QE8 22. Advanced Simulation Techniques
10
- 22.1
  0QE8 22.1 Multi-Robot Systems — Simulating Multiple Robots
- 22.2
  0QE8 22.2 Robot Coordination and Collaborative Tasks
- 22.3
  0QE8 22.3 Distributed Simulation Across Multiple Machines
- 22.4
  0QE8 22.4 GPU-Accelerated Simulation for Maximum Performance
- 22.5
  0QE8 22.5 Real-Time Factor — Measuring Simulation Speed
- 22.6
  0QE8 22.6 Headless Mode — Running Without GUI
- 22.7
  0QE8 22.7 Scripting and Automation for Experiments
- 22.8
  0QE8 22.8 Batch Processing of Multiple Simulations
- 22.9
  0QE8 22.9 Checkpointing — Saving Simulation State
- 22.10
  0QE8 22.10 Deterministic Simulation for Reproducible Results
0QE8 0QE8 23. Realistic Environments Design
10
- 23.1
  0QE8 23.1 Scene Composition — Building Complex Scenes
- 23.2
  0QE8 23.2 Importing 3D Models — Formats and Optimization
- 23.3
  0QE8 23.3 Materials and PBR — Physically Based Rendering
- 23.4
  0QE8 23.4 Lighting Setup — Realistic Illumination
- 23.5
  0QE8 23.5 Backgrounds and Skyboxes
- 23.6
  0QE8 23.6 Procedural Generation of Scenes
- 23.7
  0QE8 23.7 Warehouse Environments — Industrial Design
- 23.8
  0QE8 23.8 Kitchen and Household Environments
- 23.9
  0QE8 23.9 Factory and Industrial Environments
- 23.10
  0QE8 23.10 Outdoor Environments — Terrain and Weather
0QE8 0QE8 24. Performance Optimization
9
- 24.1
  0QE8 24.1 Profiling — Measuring Performance
- 24.2
  0QE8 24.2 Identifying Bottlenecks
- 24.3
  0QE8 24.3 Level of Detail (LOD) Strategies
- 24.4
  0QE8 24.4 Collision Mesh Optimization
- 24.5
  0QE8 24.5 Physics Substeps — Balancing Accuracy and Speed
- 24.6
  0QE8 24.6 GPU Memory Management
- 24.7
  0QE8 24.7 Batching Strategies for Efficiency
- 24.8
  0QE8 24.8 Optimizing Parallel Environments
- 24.9
  0QE8 24.9 Network Latency in Distributed Simulation
0QE8 0QE8 25. Extensions and Customization
7
- 25.1
  0QE8 25.1 What Are Omniverse Extensions
- 25.2
  0QE8 25.2 Creating Your First Extension
- 25.3
  0QE8 25.3 UI Customization — Adding Custom Panels
- 25.4
  0QE8 25.4 Custom Physics — Extending Physical Components
- 25.5
  0QE8 25.5 Custom Sensors — Developing New Sensor Types
- 25.6
  0QE8 25.6 Packaging and Distributing Extensions
- 25.7
  0QE8 25.7 Using Third-Party Community Extensions
0QE8 0QE8 26. Computer Vision Processing
8
- 26.1
  0QE8 26.1 Synthetic Data Generation for Training
- 26.2
  0QE8 26.2 Semantic Segmentation
- 26.3
  0QE8 26.3 Instance Segmentation
- 26.4
  0QE8 26.4 Bounding Box Annotation and Auto-Labeling
- 26.5
  0QE8 26.5 Depth Estimation and Depth Map Generation
- 26.6
  0QE8 26.6 Optical Flow — Estimating Motion
- 26.7
  0QE8 26.7 Object Tracking Across Frames
- 26.8
  0QE8 26.8 Pose Estimation of Objects
0QE8 0QE8 27. Real Projects and Cases
6
- 27.1
  0QE8 27.1 Project — Autonomous Object Sorting on a Conveyor
- 27.2
  0QE8 27.2 Project — Mobile Delivery Robot in an Office
- 27.3
  0QE8 27.3 Project — Collaborative Assembly with Two Manipulators
- 27.4
  0QE8 27.4 Project — Humanoid Robot Simple Locomotion
- 27.5
  0QE8 27.5 Project — Drone Autonomous Indoor Navigation
- 27.6
  0QE8 27.6 Project — VLA-Controlled Robot via Natural Language
0QE8 0QE8 28. Testing and Validation
6
- 28.1
  0QE8 28.1 Unit Testing for Robotics Code
- 28.2
  0QE8 28.2 Scenario Testing with Simulation Scenarios
- 28.3
  0QE8 28.3 Performance Testing and Evaluation
- 28.4
  0QE8 28.4 Safety Testing and Verification
- 28.5
  0QE8 28.5 Regression Testing — Preventing Degradation
- 28.6
  0QE8 28.6 CI/CD for Robotics Projects
0QE8 0QE8 29. Sim-to-Real Transition
8
- 29.1
  0QE8 29.1 The Sim-to-Real Gap — Simulation vs. Reality
- 29.2
  0QE8 29.2 Domain Randomization Strategies
- 29.3
  0QE8 29.3 Reality Gap Mitigation Techniques
- 29.4
  0QE8 29.4 System Identification — Calibrating Models
- 29.5
  0QE8 29.5 Hardware-in-the-Loop (HIL) Testing
- 29.6
  0QE8 29.6 Deployment to a Real Robot
- 29.7
  0QE8 29.7 On-Robot Testing and Live Debugging
- 29.8
  0QE8 29.8 Continuous Learning from Real Data
0QE8 0QE8 30. Career and Future Development
10
- 30.1
  0QE8 30.1 Career Paths in Robotics — Research, Engineering, Product
- 30.2
  0QE8 30.2 Building a Project Portfolio for Employers
- 30.3
  0QE8 30.3 Contributing to Open Source
- 30.4
  0QE8 30.4 Publications and Conferences
- 30.5
  0QE8 30.5 Advanced Topics — Optimal Control and Theory
- 30.6
  0QE8 30.6 Community and Networking in Robotics
- 30.7
  0QE8 30.7 Resources for Further Learning
- 30.8
  0QE8 30.8 Robotics Trends in 2025 and Beyond
- 30.9
  0QE8 30.9 Final Course Project — Comprehensive System
- 30.10
  0QE8 30.10 Conclusion and Next Steps

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0QE8 12.8 Observation Manager – Configuring Agent Perception

0QE8 12.10 Reward Manager – Reward Function Design

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