Robotics Simulation & Digital Twins Training in Ontario

Robotics Simulation & Digital Twins Training in Ontario, Canada

Master robotics simulation and digital twin technologies to design, test, and optimize robotic systems in virtual environments. Gain hands-on expertise in industry-standard tools and methodologies for real-world deployment.

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Robotics Simulation & Digital Twins Training is a professional training program delivered by ProgNXT in Ontario, Canada. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Robotics Simulation & Digital Twins Training course with hands-on, instructor-led sessions. This comprehensive training program equips participants with the essential knowledge and practical skills to leverage robotics simulation and digital...

Expert Panel

Designed and delivered by the ProgNXT Programming Expert Panel — a vetted team of industry professionals with 14+ years of hands-on experience in Python, Java, C++, and full-stack application development

ProgNXT Programming Expert Panel

Course Code: HGW93
21Hrs
Last Updated: September 2026

Course Rating5/5

This comprehensive training program equips participants with the essential knowledge and practical skills to leverage robotics simulation and digital twin technologies across various industries. Participants will explore the fundamentals of robot modeling, sensor simulation, and virtual commissioning, progressing to advanced topics such as physics-based simulation, real-time digital twins, and integration with control systems. The course emphasizes hands-on application using open-source and industry-relevant tools, enabling learners to create accurate digital replicas of robotic systems, simulate complex scenarios, and optimize performance before physical deployment.

By the end of the program, participants will be prepared to implement simulation-driven workflows that reduce development costs, minimize risks, and accelerate innovation in robotics and automation projects.

Basic understanding of robotics concepts, including kinematics and dynamics
Familiarity with programming languages such as Python or C++
Fundamental knowledge of linear algebra and calculus
Experience with 3D modeling or CAD software is beneficial but not mandatory
Willingness to engage with open-source simulation tools and environments

Upon completion of this training, participants will possess the expertise to design, implement, and manage robotics simulation and digital twin solutions effectively. They will be capable of creating accurate virtual models of robotic systems, simulating complex interactions, and leveraging digital twins for real-time monitoring and predictive maintenance. Participants will also gain the skills to integrate simulation with control frameworks, optimize performance, and deploy digital twins in industrial settings, thereby enhancing operational efficiency and innovation.

Create and configure robotic models in simulation environments
Simulate sensors such as cameras, LiDAR, and IMUs
Implement physics-based interactions and collision handling
Develop digital twins for real-time monitoring and control
Integrate simulation with ROS and other control frameworks
Optimize simulation performance for complex scenarios
Validate and verify robotic systems using virtual testing
Deploy digital twin solutions for industrial applications

Robot modeling and kinematics simulation
Sensor simulation and data fusion
Physics engines and collision detection
Digital twin architecture and synchronization
Virtual commissioning and testing
ROS integration and simulation frameworks
Performance optimization and validation
Deployment strategies for digital twins

Participants who successfully complete the training will receive an ProgNXT Certification in Robotics Simulation & Digital Twins Training, validating their expertise in:

Robot modeling and simulation
Sensor simulation and perception
Digital twin development and integration
Virtual commissioning and testing
Performance optimization and deployment

Foundations of Robotics Simulation

Introduction to simulation in robotics
Benefits and use cases across industries
Overview of simulation tools and environments
Setting up a simulation workspace

Hands-on exercises

Robot Modeling and Kinematics

Representing robot geometry and kinematics
Forward and inverse kinematics in simulation
URDF and SDF formats for robot description
Building a simple robot model

Hands-on exercises

Sensor Simulation and Perception

Simulating cameras, depth sensors, and LiDAR
Modeling sensor noise and imperfections
Point cloud generation and processing
Sensor fusion techniques in simulation

Hands-on exercises

Physics Simulation and Dynamics

Physics engines and their characteristics
Collision detection and response
Contact modeling and friction
Simulating actuators and dynamics

Hands-on exercises

Digital Twin Concepts and Architecture

Defining digital twins and their components
Data synchronization between physical and virtual
Real-time communication protocols
Cloud and edge architectures for digital twins

Hands-on exercises

Virtual Commissioning and Testing

Virtual commissioning workflows
Automated testing in simulation
Scenario-based validation
Integration with PLC and control systems

Hands-on exercises

ROS and Simulation Integration

ROS architecture and simulation interfaces
Using Gazebo and other ROS-compatible simulators
Bridging simulation and ROS control
Developing ROS nodes for simulation

Hands-on exercises

Advanced Simulation Techniques

Multi-robot simulation and coordination
Human-robot interaction simulation
Machine learning integration for perception
Domain randomization and transfer learning

Hands-on exercises

Performance Optimization and Validation

Profiling simulation performance
Optimizing models and scenes
Validation metrics and benchmarks
Ensuring fidelity and accuracy

Hands-on exercises

Deployment and Real-World Applications

Deploying digital twins in production
Case studies in manufacturing and logistics
Maintenance and lifecycle management
Future trends in simulation and digital twins

Hands-on exercises

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

Target Audience

Robotics engineers and developers
Automation and control engineers
Simulation specialists
Researchers and academics in robotics
Professionals in manufacturing, logistics, and healthcare

Evaluation

Continuous assessment through hands-on exercises
Quizzes to test theoretical understanding
Final project or case study evaluation
Participation in discussions and problem-solving activities

Robotics Simulation & Digital Twins Training Events in ontario

Online Events

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More Upcoming Session Dates

October 2026

📍 Ontario, Canada

7th Oct 2026→9th Oct 2026

🌐 Online Instructor-Led

12th Oct 2026→14th Oct 2026

🌐 Online Instructor-Led

23rd Oct 2026→25th Oct 2026

📍 Ontario, Canada

28th Oct 2026→30th Oct 2026

November 2026

📍 Ontario, Canada

4th Nov 2026→6th Nov 2026

🌐 Online Instructor-Led

9th Nov 2026→11th Nov 2026

📍 Ontario, Canada

18th Nov 2026→20th Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→26th Nov 2026

December 2026

📍 Ontario, Canada

1st Dec 2026→3rd Dec 2026

🌐 Online Instructor-Led

6th Dec 2026→8th Dec 2026

🌐 Online Instructor-Led

18th Dec 2026→20th Dec 2026

📍 Ontario, Canada

19th Dec 2026→21st Dec 2026

Events for other courses in Ontario, Canada

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Robotics Simulation & Digital Twins Training in Ontario