[Question] Why Good Bipedal Locomotion Performance on Rough Terrains on Issaclab

[MightyCrane]

I have a question about the training setups on complex terrains such as stairs for bipedal robots in IsaacGym versus IsaacLab. In many published papers using IsaacGym, authors employ very complex designs and configuration parameters (e.g., reward scaling, teacher–student distillation networks) and train for tens of thousands of iterations. But some show shaky walking performance. By contrast, I’ve noticed that IsaacLab provides tasks like Isaac-Velocity-Rough-H1-Play-v0 and Isaac-Velocity-Rough-G1-v0 which—using common reward functions and
intuitive configuration parameters, and just a few thousand iterations—can achieve relatively stable stair-climbing and rough-terrain walking within a day, without any explicit tricks.

I would greatly appreciate any guidance you could offer on:

1. Are there particular aspects of IsaacLab’s physics modeling or contact handling that help promote learning on uneven terrain?
2. Beyond the visible settings, are there any practical tricks used when training these tasks?
3. Could you point me to any documentation or papers that explain the factors behind this level of performance?

[RandomOakForest]

Thank you for posting this. The idea is a great post for our Discussions section. I'm going to move this post to that section for the team to follow up.