From Fusion 360 to IsaacLab: training a custom robot with reinforcement learning
Hi everyone,
I recently worked on a small project where I designed a custom robot in Fusion 360 and trained it in IsaacLab using reinforcement learning.
The robot is a wheeled biped-style platform. After creating the CAD model, I converted it into a simulation-ready asset, set up the joints, and used it for stabilization and jump-recovery tasks in IsaacLab.
What I found most interesting was how much the physical design affects the learning process. Things like joint placement, link length, wheel contact, collision shapes, inertia, and actuator settings all had a noticeable impact on whether the robot could learn stable behavior.
The first task was basic stabilization, where the robot learns to maintain its posture. I also tested a jump-and-stabilize task, where the robot needs to recover after a more dynamic motion.
This made me realize that building a robot for RL is not just about making a nice-looking CAD model. The morphology, physics properties, and simulation setup are all part of the learning problem.
The workflow was roughly:
Fusion 360 → asset preparation → joint setup → IsaacLab training → policy evaluation
I’m planning to extend this robot to more tasks, including wheeled balance control, push recovery, locomotion, turning, navigation, and object interaction.
I wrote a longer post with more details about the design process and what I learned from training it in IsaacLab.