How to train <task-name>-real?

[shaosb]

Thank you for open-sourcing such an amazing project! While attempting to reproduce the code, I encountered a few issues and would appreciate your clarification:

1. During the training of pick-real, the environment does not initialize a table, causing the target object to fall directly to the ground. This makes it impossible for the robot to grasp the object. Is this a bug or an intentional training feature?

2. In pick-real, the observation is 100-dimensional, with rel_pose_object comprising 7 dimensions (3 for position and 4 for quaternion orientation). However, in the MuJoCo environment, the observation is 96-dimensional, and rel_pose_object only includes 3 dimensions (position, without orientation). This discrepancy prevents the weights trained in pick-real from running in MuJoCo. Is this intentional, or is there an updated version of pick-real? Additionally, the deploy_pick script in MuJoCo also encounters the issue of objects falling, making grasping impossible.

3. In motion_lib, when loading reference motion data, a motion_offset is used to define a positional bias. Specifically, the x-axis of the grasping position is set to 2.1. Does this mean the robot can only grasp objects at x = 2.1, or is there another reason for this value? Given that pick-real currently does not train correctly, this is quite confusing.

4. The paper and code do not clearly explain how the robot generalizes to grasping objects at positions different from the reference motion. Could you provide some insights or demos on this?

5. The project includes HumanoidVerse, and the demo interface also shows some demos from HumanoidVerse. However, the README file does not cover how to reproduce or use these parts. Could you please make these methods publicly available?

Thanks for sharing this great project. Merry Christmas! Bese wishes.

[dvij542]

I appreciate your interest in DreamControl!

1. Yes, you are right, there should be a platform during training. This is something I missed while refactoring the code; I have now resolved it. Sorry about the error. You should be able to train the policy now. Please let me know if you still face any issues
2. Resolved it now. Again, this is something I missed while refactoring the code
3. 2.1 is an arbitrary value chosen to get a good camera view for eval video. It could be set to any other value. The reason behind standardizing the x position of the object is that IsaacLab does not directly support moving (or respawning) non-rigidbody objects during training for each new episode. As I want the platform to be static and not a rigidbody, what I did was move the starting point of the motion to be able to fix the platform at 2.1 (or any other fixed value). Let me know if you need more explanation
4. The idea is that you generate a large no of motions, randomizing the object grasp positions within a distribution, and use those as a reference to train a policy to grasp an object within that range. During test time, the policy should generalize to grasping an object located within the same range as training. Although the sampled positions may not be directly present in the set of generated trajectories used for training, the no of those trajectories used for training is large enough to force the policy to generalize to any other position within the same distribution
5. The code only uses USD files from humanoidverse. I have removed all other scripts from the repo to avoid confusion. Thanks for pointing that out!

Let me know if you run into other issues. Belated Merry Christmas!

Best,
Dvij