improbable-ai / dribblebot Goto Github PK

Question about _apply_drag_force() method and reward stability:

Hi, thanks for sharing the source code of such an interesting and impressive work!

I've been exploring the code, and I noticed something that raised a question. In the LeggedRobot class, there is an _apply_drag_force() method. However, it seems this method is actually unused during simulation, which is different from what's described in the original paper. Is the open-sourced code is a modified version or it is just because the force is forgotten to be applied in the code?

Additionally, when attempting to reproduce the results using the provided training script, I encountered some issues with the stability of the reward curve. Is this expected behavior, or is there something I might be missing?

Thank you for your attention to this issue.

Hello, thanks for your impressive and outstanding work and open sourcing the training code.

I would like to share my sim-to-real deployment code here since the official deployment toolkit is still in preparation.

https://github.com/YouJiacheng/dribble-deploy

My code only uses 1 camera but can be extended to 2 cameras easily.

Hi,

I have a Nvidia Quadro p2000 GPU with 5GBs of memory with Ubuntu 18.04, CUDA 11.6 and driver version: 510.39.01.
I tried the training with num_envs = [16, 100, 500, 4000] and non of them is working with the following error:

internal error : PhysX Internal CUDA error. Simulation can not continue!
[Error] [carb.gym.plugin] Gym cuda error: an illegal memory access was encountered: ../../../source/plugins/carb/gym/impl/Gym/GymPhysX.cpp: 1459
Segmentation fault (core dumped)

It seems the resources are not enough and while reducing num_envs didn't solve the problem. Any help?

Note, I tried CUDA 11.3 as recommended by the README and a compatability error said that at least 11.4 is required. After trying 11.4 with pytorch 11.3, same error happened. So I decided to upgrade CUDA to 11.6 and the same for the pytorch (pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
).
Log.txt

Hi,

Thank you for the amazing work!

While experimenting with your code, despite running the training multiple times, we're observing stability issues. Here is an example of one of the rew_total graphs:

Is this behavior expected or indicative of an underlying problem? Is the maximum total reward achieved here (around 350) the same as you got? Additionally, if you could share the graphs from one of your runs it might help us to track down the issue and understand the expected behavior.

Thanks!