NVIDIA Omniverse (Isaac Sim) Training in Metro Manila, Philippines

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.

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