Skill Tree
- Robot Learning: policy gradients, offline RL, model-based RL
- Motion Planning: sampling-based, optimization-based, constraints
- Control: PID, LQR, MPC
- ROS2: nodes, TF, launch, tooling
- Sim Platforms: Isaac Sim/Lab, Mujoco, Gazebo
- MLOps: data/versioning, training pipelines, eval dashboards
Suggested Learning Path
- Control fundamentals and dynamics basics.
- Motion planning algorithms and constraints.
- Deep RL foundations and PPO/SAC practice.
- Sim2real and domain randomization.
- System integration in ROS2.
