Comments (11)
benelot
commented on August 22, 2024
2
I am basically the maintainer of the envs in bullet3. However, since I have
no push rights to bullet3 I prepare stuff here and then prepare a pull
request. There was some discussion to make the implementations separate
from the bullet3 repo, but for now they are kept in there.
…On Sun, Nov 25, 2018, 04:09 Dan Brickley ***@***.***> wrote:
what is the relationship between this repo and
https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym
?
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josiahls
commented on August 22, 2024
1
Since people are posting here, I want fetch support for my research, so I am in the middle of porting the fetch robot and will be doing extensive env setup. If anyone wants to help, I will be making branches on my fork, then doing pull requests to this repo.
Also the slack link is dead
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benelot
commented on August 22, 2024
1
Slack channel invite link is updated! Thanks for telling me again. I think I turned off link expiration, but it seems to have expired nonetheless.
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benelot
commented on August 22, 2024
Hey there! I am very happy that you are interested in contributing! The
general theme of pybullet-gym is to free gym from the commercial mujoco
engine. But it means that every gym must behave nearly equally to the
original env in order to transfer the trained weights over from a mujoco
env to the pybullet env. I will soon provide trained agents on the mujoco
envs that can be used as benchmarks.
Here is a rough description of things that need to be done:
-[ROBOSCHOOL GYMS] The current gyms are the roboschool gyms ported to
pybullet. So far, most of them work well, except for the manipulator envs
striker, pusher and thrower, where the robot is not correctly loaded. This
will have to be fixed with Erwin Coumans, which is something I can report
to him.
-[OPENAI MUJOCO GYMS] Soon I will start to port the OpenAI gyms, which
unfortunately have a slightly different observation (and probably action)
vector. I can setup all the gyms quickly, but it will take a while to find
out what some of observations are in mujoco and what they correspond to in
pybullet. Some of the observations might not be exposed on pybullet, then
we can request them, for others it is already hard to know what they are in
mujoco.
-[OPENAI ROBOTICS GYMS] Next in line would be the robotics gyms in OpenAI.
These are particularly delicate simulations and might take some tuning to
even be simulatable in pybullet.
-[DEEPMIND CONTROL SUITE] Then there is Deepmind Control Suite, another set
of gyms which are in mujoco and need to be freed. I did not look into those
yet, but I am sure there are some tough parts too.
-[STANFORD SUITE] https://github.com/StanfordVL/robosuite
I will turn this list into a cleaner roadmap on the readme page for others
who might be interested in making contributions.
Do you have some experience with Mujoco? You can definitely be of help
since there is a lot to be done! Thanks!
…On Mon, May 28, 2018, 22:28 Jon Michaux ***@***.***> wrote:
I am interested in contributing to this project. Do you want to lay out a
more detailed roadmap for others who might be interested in making
contributions?
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novinsh
commented on August 22, 2024
Can you provide some better channel of communication, like on slack to get in touch more easily?
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benelot
commented on August 22, 2024
I am about to go on holidays. I will provide you with a slack channel by
the beginning of october.
…On Sat, Sep 15, 2018, 13:32 Novin Shahroudi ***@***.***> wrote:
Can you provide some better channel of communication, like on slack to get
in touch more easily?
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benelot
commented on August 22, 2024
Hey there! I opened a slack channel for us to collaborate:
The most important thing to do at first is to rebuild all mujoco environments in pybullet. This is most easily done by taking the structure from the roboschool code and replacing inputs and outputs to match the inputs/outputs of an agent trained in mujoco.
If somebody has openai gym with mujoco installed, it would be great if somebody could train some tensorforce agents on it to perform well. Then we can connect them to the above built envs to tune them to behave similarly.
One of the main problems is that for some environment readings to the agent in mujoco I can not find the corresponding reading in pybullet. But maybe we can figure this out with the pybullet/bullet maintainer Erwin Coumans.
I can invest some time this week to do the first task.
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jmichaux
commented on August 22, 2024
Great! I need to look into installing MuJoCo. Currently the CPU on my research machine doesn't have the AVX instruction set required to run MuJoCo. I'll post any further updates to the slack channel.
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danbri
commented on August 22, 2024
what is the relationship between this repo and https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet/gym ?
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benelot
commented on August 22, 2024
Slack channel invite link is updated! Thanks for telling me, I forgot to turn off link expiration.
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floringogianu
commented on August 22, 2024
@benelot Any chance you could activate the slack invite again? Looking forward to using this env in the next period and I hope to contribute to it.
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Related Issues (20)
- pybullet build error on macos 10.15.5 HOT 1
- Why are there references to extra "non-existent" links? HOT 2
- The bulit-in texture doesn't load correctly HOT 2
- HopperMuJoCoEnv HOT 7
- Feature request / Would like to contribute: Competitive Environments
- Rendering (mode='rgb_array') error
- How to Cite PyBullet-Gym HOT 2
- How to get the timestep of an environment? HOT 2
- How to set the environment status manually? HOT 2
- Recommended way to change the robot mass HOT 1
- Stopping render - relation between the simulation and rendering HOT 1
- AntMuJoCoEnv-v0 contains many unnecessary states. HOT 13
- robot out of view when using env.render('rgb_array')
- how to connect two arms' end-effectors
- New environment request: OpenAI's Safety Gym
- Python kernal crashing HOT 4
- apparent environment seed issues with ant environment
- Any suggestions on how to lock certain joints?
- AttributeError: 'HopperBulletEnv' object has no attribute '_p'
- Which version of gym does this lib support? HOT 1
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