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reopening #50 - End effector delta #52
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Hi, Few questions to get down to the problem:
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I’m sampling x,y,z each from -1 to 1, and dividing by 100. I then use this as the delta to update. |
Hi,
This is your problem. Remember, you are using the |
No, I edited in the delta_ee_pose code to set the resulting action to just use the same quaternion without modifyinh |
If you've modified the backend then I need to see your changes. Please post the changed lines here |
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Thanks. And how many steps does it run for before you get a (what I assume is) InvalidActionError? |
So If i step with [1,1,1,1(gripper)] which is [0.01...] after the division, then I get
on the first step. |
BTW, as an aside, I believe:
can just be written as |
Also, did some more digging and the 2 seconds only occurs when it can't find a path, when it does find one it's about 0.2 seconds. |
This makes sense, right? If you look at the starting configuration of the arm, the end effector cant really increase its z axis anymore without also altering the rotation, and so it will not be able to find a valid configuration. If you were to negate the z axis (which would send the end effector down), then you would find that it would run for longer before getting the error. |
This is because it keeps trying to find a path/configuration until some max_attempts limit. |
Ah, thanks Stephen, makes sense. |
So just to elaborate:
Hope that helps :) |
Yeah, I'm trying to set up an env to match Mujoco's Fetch Reach task to baseline some earlier experiments on RLBench. So, most likely rather than configuring the agent for now I'm trying to adjust the environment to be similar. Thanks again for the help! It'd be great if you could take a look at the stepping speeds in #53 , since without faster speeds we won't be able to use this environment :( |
No probs 👍 |
I'm not sure if this was addressed in other areas, but our project group ran into this issue and got training to run smoothly by manually forcing change in EE position to have a maximum magnitude. See here for more details. These errors can still happen though, especially when the model has not converged. We handled two errors separately. ConfigurationPathError implies the desired action (delta EE pos) is too large. InvalidActionError implies the desired actions brings the EE outside its configuration space (ex: literally reaching beyond the arm's max reach). Please see here for how we handled this. Hope this helps anyone facing these issues. |
@Alvinosaur Your team's solution seems reasonable! Did your team have investigated the IK solvers of other RL platform like Pybullet, Robosuite, ... ? I think addressing these IK errors occurred from explorative Cartesian space 6 DoF actions is a big challenge in robot learning domain... |
Continuation of #50 :
There are still errors with small delta (0.01). It is also really slow - taking around 2 seconds to find a path. Is this expected behavior?
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