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Chapter 3: Simulation Environments

🚧 Status: Planned (Spec → Plan → Tasks Complete, Awaiting Implementation)​

This chapter covers high-fidelity robot simulation using Gazebo, Unity, and NVIDIA Isaac Sim.

→ View Spec on GitHub

→ View Plan on GitHub

→ View Tasks on GitHub

📖 Planned Content​

Learning Objectives​

  1. Configure Gazebo Classic and Gazebo Harmonic for humanoid simulation
  2. Integrate Unity ML-Agents with ROS 2 for reinforcement learning
  3. Deploy NVIDIA Isaac Sim for GPU-accelerated physics
  4. Compare simulation platforms and select appropriate tool for use case
  5. Migrate simulation artifacts to physical robots

Topics Covered​

  • Gazebo world design and sensor models
  • Unity ML-Agents for policy training
  • Isaac Sim synthetic data generation
  • Physics parameter tuning (gravity, friction, timestep)
  • Sensor noise modeling (IMU, LiDAR, cameras)

Hands-On Labs​

  • Lab S1: Gazebo humanoid spawn and control
  • Lab S2: Unity obstacle navigation with reinforcement learning
  • Lab S3: Isaac Sim camera calibration and synthetic dataset

🔗 Development Artifacts​

Ready for: /sp.implement when time permits

This chapter is fully spec'd and planned. Implementation pending as part of textbook expansion.