LeRobot, Policy Training & Real-World Deployment
Collect datasets in simulation and on real robots, train manipulation policies with LeRobot, and deploy learned models back onto physical arms.
Course overview
The course culminates in data-driven robot learning. Students collect LeRobot-format datasets from teleoperation and simulation, train manipulation policies, study common model families, and deploy learned models back to the physical arm.
Core curriculum
Four themed modules. Each module is a working block of lessons and labs.
Dataset Creation
Use Isaac Sim and real-robot demonstrations to build structured datasets with observations, actions, rewards, and task metadata.
Policy Training
Train and evaluate LeRobot policies for manipulation, including imitation-learning baselines and modern policy architectures.
Model Families
Compare common model families for robotics, such as ACT, diffusion-style policies, and lightweight vision-language-action approaches.
Deployment & Eval
Deploy trained models onto the arm with safety wrappers, runtime monitoring, and real-world evaluation of success, latency, and robustness.
What you'll gain
- LeRobot-format datasets from sim and real robots
- Trained manipulation policies you can demo
- Hands-on comparison of modern robotics model families
- A real-world deployment + evaluation report
Ready to build with AI?
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