Optimize RL Snap-Fit Assembly

I already have a fully working Isaac Lab simulation of a Franka-style arm inserting a snap-fit buckle; component deformability is modelled and the environment, CAD geometry, and basic reward logic are all in place. The stumbling block is the policy itself: the robot still fails to complete the assembly because it chronically misaligns the male and female parts.

I’d like you to rethink and implement the Deep RL side so that the arm can carry out the join quickly and repeatably, even when the scene is perturbed (e.g. slightly different start poses or offsets in the gripper and buckle position). A solid solution will probably involve re-shaping the reward, introducing curriculum or domain randomisation, and tuning hyper-parameters, but I’m open to whatever method you feel is most robust. You are free to use PPO, SAC, RL-Games, or your preferred framework so long as it plugs cleanly into Isaac Lab.

Deliverables should include:
• Updated training script(s) and reward function integrated with my existing repo.
• A brief README describing the changes and how to reproduce training.
• A short evaluation run or video that demonstrates a high success rate with no visible misalignment errors.