- Home
- Courses
- Simulation and Digital Tools
- Isaac Lab: Custom Reinforcement Learning Environments
Curriculum
- 6 Sections
- 45 Lessons
- Lifetime
Expand all sectionsCollapse all sections
- 1. Isaac Lab Foundations6
- 1.1BONR 1.1 What Is Isaac Lab and How It Differs from Isaac Sim
- 1.2BONR 1.2 Isaac Lab Architecture Overview – Managers and Workflows
- 1.3BONR 1.3 Installing and Setting Up Isaac Lab
- 1.4BONR 1.4 Project Structure, Template Generator, and External Projects
- 1.5BONR 1.5 Understanding the Simulation Loop and Timesteps
- 1.6BONR 1.6 Debugging Isaac Lab Environments
- 2. Environment Architecture and Configuration9
- 2.1BONR 2.1 Configuration-Driven Environment Design Principles
- 2.2BONR 2.2 Asset Configuration Classes – Importing Robots
- 2.3BONR 2.3 Scene Configuration – Setup and Initialization
- 2.4BONR 2.4 Creating Ground Planes, Lighting, and Visual Elements
- 2.5BONR 2.5 Asset Spawning and Object Placement
- 2.6BONR 2.6 Environment State Management
- 2.7BONR 2.7 Reset Mechanisms and Initialization Workflows
- 2.8BONR 2.8 Custom Asset Configurations
- 2.9BONR 2.9 Working with USD Files in Isaac Lab
- 3. Managers and Task Interface8
- 3.1BONR 3.1 Observation Manager Architecture
- 3.2BONR 3.2 Defining Observation Specifications and Sensors
- 3.3BONR 3.3 Action Manager and Action Space Design
- 3.4BONR 3.4 Different Action Types – Position, Velocity, Effort
- 3.5BONR 3.5 Event Manager for Simulation Events
- 3.6BONR 3.6 Termination Manager – Episode End Conditions
- 3.7BONR 3.7 Curriculum Manager – Progressive Task Difficulty
- 3.8BONR 3.8 Reward Manager – Function Design and Callbacks
- 4. Reward Design and Task Formulation7
- 4.1BONR 4.1 Reward Function Principles for RL
- 4.2BONR 4.2 Dense versus Sparse Rewards
- 4.3BONR 4.3 Reward Scaling and Normalization
- 4.4BONR 4.4 Multi-Objective Reward Combinations
- 4.5BONR 4.5 Task-Specific Reward Design Patterns
- 4.6BONR 4.6 Debugging and Analyzing Reward Signals
- 4.7BONR 4.7 Common Pitfalls in Reward Design
- 5. Reinforcement Learning Training9
- 5.1BONR 5.1 Integrating RL Frameworks with Isaac Lab
- 5.2BONR 5.2 Stable-Baselines3 – PPO and SAC Configuration
- 5.3BONR 5.3 Training Loop Setup and Hyperparameters
- 5.4BONR 5.4 Monitoring Training with TensorBoard
- 5.5BONR 5.5 Parallel Environment Training and GPU Utilization
- 5.6BONR 5.6 Policy Checkpointing and Resuming Training
- 5.7BONR 5.7 Evaluation and Policy Testing
- 5.8BONR 5.8 Curriculum Learning – Progressive Training
- 5.9BONR 5.9 Multi-Agent Reinforcement Learning Setup
- 6. Advanced Topics and Optimization6
- 6.1BONR 6.1 Actuator Model Customization for Sim-to-Real
- 6.2BONR 6.2 Domain Randomization Implementation
- 6.3BONR 6.3 Sensor Noise and Realistic Simulation
- 6.4BONR 6.4 Performance Profiling and Optimization
- 6.5BONR 6.5 Distributed Training Across Multiple GPUs
- 6.6BONR 6.6 Deploying Trained Policies in Isaac Sim
Modal title
Main Content