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Getting Started With Isaac Lab
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Getting Started With Isaac Lab
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Table of Contents
Contents
Robot Learning Fundamentals
Motivation and Definition
Problem Specification
Forms of Learning
Reinforcement Learning
Proximal Policy Optimization
Reward Design and Evaluation
Policy Distillation
Simulation to Reality
Robot Learning Workflows
Isaac Lab Basics
The Isaac Platform
Project Generation
Development Workflow
The Isaac Lab CLI
Manager-Based Workflows
The Cartpole Example
Cartpole Training
Train a Robot Arm
Project Setup
SO-101 Robot
Vial and Rack Scene
Running Simple Agents
Train a Teacher Policy
Distill a Student Policy
Managing Experiments
Transfer Policies From Simulation to Reality
Overview
The Successes of Reinforcement Learning
Reinforcement Learning
The Challenges of Reinforcement Learning
Simulation: A Game-Changer
What Is the Reality Gap?
The Reality Gap
Visualizing the Reality Gap
Bridging the Gap: Simulation Enhancement
Domain Randomization
Visual Domain Randomization
Depth Camera Randomization
Point Cloud Perturbations
Bridging the Gap: Real-World Data Integration
System Identification
Actuator Modeling
Digital Twins
Neural Rendering
World Models
Regularization
Regularization
Leveraging Privileged Information
Considerations When Bridging the Reality Gap
Considerations
Tips for Sim-to-Real
Review
Quiz
Resources
Physical AI Learning Hub
Developer Forums
Community Discord
Index
