Course Overview
Physical AI & Robot Learning bridges the gap between deep reinforcement learning theory and real robot deployment. Using NVIDIA Isaac Lab (built on Isaac Sim), you will design Gym-compatible environments, train locomotion and manipulation policies with state-of-the-art RL algorithms, and apply sim-to-real transfer techniques so learned policies work on physical hardware. This is the course for engineers ready to push robots beyond pre-programmed routines.
Key Highlights
Course Syllabus
Learning Outcomes
Design custom Isaac Lab environments for any robot task
Train high-performance RL policies with PPO and SAC
Run massively parallel training on GPU compute
Apply domain randomisation for reliable sim-to-real transfer
Deploy learned policies to physical robots via ROS 2
Evaluate and debug RL policy performance systematically
Who Is This For?
Prerequisites
- ☑ Python proficiency (NumPy, PyTorch basics)
- ☑ Completion of Isaac Sim Essentials or equivalent
- ☑ Basic understanding of neural networks