NVIDIA Omniverse (Isaac Sim) Training in Ontario

NVIDIA Omniverse (Isaac Sim) Training in Ontario, Canada

Participants will explore the complete robotics pipeline—modeling, control, perception mapping, navigation, and deployment—using Isaac Sim, Isaac Lab, and Isaac ROS.

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NVIDIA Omniverse (Isaac Sim) Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this NVIDIA Omniverse (Isaac Sim) Training course with hands-on, instructor-led sessions. This course provides a comprehensive, hands-on introduction to NVIDIA Omniverse Isaac Simulator, enabling learners to design,...

Course Code: UKZ35
21 Hrs
Last Updated: April 2026

Course Rating5/5

This course provides a comprehensive, hands-on introduction to NVIDIA Omniverse Isaac Simulator, enabling learners to design, simulate, and deploy robotic digital twins using GPU-accelerated physics, photorealistic rendering, and AI-based perception.

Participants will explore the complete robotics pipeline—modeling, control, perception, mapping, navigation, and deployment—using Isaac Sim, Isaac Lab, and Isaac ROS, all integrated with ROS 2. The course emphasizes practical implementation, industry use cases, and sim-to-real readiness, preparing learners to build scalable robotics solutions for real-world environments.

Welcome to the official NVIDIA Omniverse (Isaac Sim) Training certification program in Ontario, Canada. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 21 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

To get the most value from this course, learners should have:

Mandatory

Basic understanding of robotics concepts (kinematics, sensors, actuators)
Working knowledge of Python programming
Familiarity with Linux (Ubuntu) environment

Recommended

Prior exposure to ROS or ROS 2
Basic understanding of computer vision and machine learning
Introductory knowledge of robot control or navigation

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

Design and simulate realistic robotic environments using NVIDIA Omniverse Isaac Sim
Build and control robots using ROS 2, Isaac ROS, and Isaac Lab
Implement AI-based perception pipelines with cameras and LiDAR
Develop real-time mapping and navigation systems using NVBlox and V-SLAM
Generate synthetic datasets to train and validate AI models
Deploy end-to-end robotic digital twins aligned with industry standards
Apply simulation-based workflows to autonomous robots, manipulators, and AMRs

Upon successful completion of the course, learners will acquire the following skills:

Simulation & Digital Twins

Create realistic robotic digital twins using Isaac Sim
Model robots, environments, and physics-accurate interactions
Use USD-based workflows for scalable scene design

Robot Control & ROS Integration

Integrate robots with ROS 2 and Isaac ROS
Implement teleoperation and autonomous control pipelines
Use Isaac Lab for manipulation and learning-based control

Perception & Sensor Processing

Configure and simulate cameras, LiDAR, IMU, and depth sensors
Perform sensor fusion and real-time DNN inference
Implement autonomous perception pipelines

Mapping & Navigation

Build GPU-accelerated 3D maps using NVBlox
Implement Visual SLAM (V-SLAM) pipelines
Develop navigation-ready digital twins

Advanced Robotics Applications

Generate synthetic datasets with domain randomization
Implement language-conditioned and vision-language control
Deploy end-to-end digital twin workflows

Learner progress can be evaluated using a mix of practical labs, quizzes, and a capstone project, covering:

Conceptual Assessments

Digital twin concepts and simulation workflows
Isaac Sim architecture and Omniverse components
ROS 2 and Isaac ROS integration principles
Perception, SLAM, and navigation fundamentals

Hands-on Assessments

Robot and environment modeling in Isaac Sim
ROS 2 teleoperation and control implementation
Sensor configuration and fusion pipelines
NVBlox-based 3D mapping
Synthetic data generation and deployment workflows

Capstone Evaluation

Design and deploy a complete robotic digital twin
Integrate perception, mapping, and navigation
Demonstrate sim-to-real readiness

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

Foundation

Introduction to Robot Simulation & Digital Twins

Need for simulation in robotics
Digital twins: concepts, benefits, and real-world use cases
Simulation vs real-world testing
Overview of NVIDIA Omniverse ecosystem
Industry examples (warehouse, autonomous vehicles, manipulation)

Isaac Sim Architecture & Features

Omniverse Kit architecture
USD (Universal Scene Description) fundamentals
Physics engine (PhysX)
Rendering, RTX, and real-time simulation
Extension-based workflow

Introduction to Isaac Lab

What is Isaac Lab and how it differs from Isaac Sim
RL-ready simulation architecture
Task, environment, and agent abstractions
Supported control pipelines

Introduction to Isaac ROS

Isaac ROS overview and packages
Hardware acceleration with NVIDIA GPUs
Integration with ROS 2
Typical perception and navigation pipelines

Core Modeling & Control

Robot, Scene & Environment Modeling

Importing robots (URDF, USD)
Creating environments and assets
Physics properties and articulation setup
Lighting, materials, and collision modeling

ROS 2 Teleoperation and Control

ROS 2 node communication in Isaac Sim
Teleoperation using keyboard/joystick
Publishing and subscribing to robot states
Control loops and latency considerations

Manipulator Control using Isaac Lab

Joint-space and task-space control
Action spaces and observations
Running scripted policies
Evaluating performance

Isaac Sim ROS 2 Bridge

ROS 2 Bridge architecture
Data synchronization and clock handling
Supported message types
Debugging common issues

Integration Lab

Combining modeling + control + ROS 2 bridge
Full robot simulation pipeline

Perception & Sensors

Sensors in Isaac Sim

Camera types (RGB, depth, stereo)
LiDAR and IMU simulation
Sensor noise and realism
Sensor placement best practices

Camera–LiDAR Sensor Fusion

Sensor calibration concepts
Time synchronization
Fusion pipelines
Visualization of fused data

Real-Time DNN Inference for Object Detection

Running pre-trained DNNs in Isaac Sim
Isaac ROS DNN inference pipeline
Bounding box and segmentation outputs
Performance benchmarking

Autonomous Driving Perception

Perception stack for autonomous robots
Lane detection, obstacle detection
Simulation for AV testing
Limitations and validation

Mapping & Navigation

Introduction to NVBlox

What is NVBlox?
GPU-accelerated 3D reconstruction
Use cases in navigation and manipulation

NVBlox for Real-Time 3D Mapping

Integrating depth/LiDAR data
TSDF and ESDF maps
Visualization and updates

Introduction to Visual SLAM

SLAM fundamentals
Visual vs LiDAR SLAM
Challenges in simulation vs real world

Visual SLAM

Running V-SLAM pipelines
Trajectory estimation
Map quality evaluation

Navigation Digital Twin

Navigation stack integration
Costmaps and path planning
Testing navigation scenarios

Advanced Topics & Applications

Synthetic Dataset Generation & Domain Randomization

Synthetic data generation workflows
Annotation pipelines
Domain randomization strategies
Improving sim-to-real transfer

Natural-Language & Vision-Language Control

Language-conditioned robot control
Vision-language models (VLMs)
Prompt-based interaction with robots

End-to-End Digital Twin Deployment

Simulation - deployment workflow
Validation and testing strategies
Scaling digital twins
Industry deployment case studies

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants will be evaluated based on: Daily quizzes, mock questions and query resolution. Group Discussion and Case Study Discussion. Assessment will be through hands-on labs, quizzes, and a hands-on exercises.

ProgNXT Certificate of Completion will be awarded to participants who complete the course and pass the evaluations.

NVIDIA Omniverse (Isaac Sim) Training Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

📍 Ontario, Canada

18th Oct 2026→20th Oct 2026

🌐 Online Instructor-Led

22nd Oct 2026→24th Oct 2026

November 2026

🌐 Online Instructor-Led

4th Nov 2026→6th Nov 2026

📍 Ontario, Canada

11th Nov 2026→13th Nov 2026

📍 Ontario, Canada

23rd Nov 2026→25th Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→29th Nov 2026

December 2026

🌐 Online Instructor-Led

1st Dec 2026→3rd Dec 2026

📍 Ontario, Canada

8th Dec 2026→10th Dec 2026

📍 Ontario, Canada

24th Dec 2026→26th Dec 2026

🌐 Online Instructor-Led

28th Dec 2026→30th Dec 2026

Events for other courses in Ontario, Canada

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NVIDIA Omniverse (Isaac Sim) Training in Ontario