Comments (4)
Thanks for your issue. The reindex function is mainly used to match the dof oder on hardware since unitree uses slightly different ordering. I have checked to use new_go1.urdf with 051-40 and there is no issue.
Screen.Recording.2023-09-27.at.22.39.32.mov
Are you running with 051-41 or 051-42? Policies with depth images won't work out of the box because if you don't change the camera position, the camera on go1 will be inside the robot so the depth is always black. You can try to move the camera to a proper location. see here.
Screen.Recording.2023-09-27.at.22.41.47.mov
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Thanks for your reply! The problem is just as you say. I change the camera position to [0.3,0,0.03] and the view is no longer black, now I want to train a depth distillation policy with this new camera position on go1, (I use 051-40 as the base policy ) however, the depth loss is not declining, is there any problem?
the loss is as follows:
my training command is this:
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Make sure the camera position is appropriate so the robot is able to see the obstacles in front. You can also try without direction distillation, which is easier to train.
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hi, I am also interesting in training the policy with go1, so have you successfully train the policy with go1 ? Can you share the appropriate camera coordinates? Thanks a lot!
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Related Issues (20)
- how many envs do you use when running the train.py? HOT 2
- Some questions about distillation HOT 8
- CUDA error: device-side assert triggered HOT 1
- Code migration
- Collection Time of per iteration HOT 8
- torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 106.00 MiB (GPU 0; 11.77 GiB total capacity; 6.69 GiB already allocated; 86.62 MiB free; 6.74 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF HOT 2
- KeyError: 'depth_actor_state_dict' HOT 3
- Blank Depth image screen for distillation policy video HOT 1
- The Loss_depth/depth_encoder is 0 when i train distillation policy
- How to deploy on a real robot HOT 5
- Delay difference on paper and code HOT 1
- After running, you can't see the simulation interface HOT 2
- There is no graphical interface after the code runs, can you help me? thank you. HOT 1
- Questions about step5 in README.md file HOT 4
- evaluation.py code
- the meaning of parameter
- A1 部署问题汇总 HOT 18
- AttributeError: 'env' object has no attribute 'n_priv_latent'
- Run error HOT 1
- Gym cuda error: an illegal memory access was encountered When using --device=cuda:1 HOT 1
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