MuJoCo_RL_UR5
A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances. (by PaulDanielML)
IsaacGymEnvs
Isaac Gym Reinforcement Learning Environments (by NVIDIA-Omniverse)
| MuJoCo_RL_UR5 | IsaacGymEnvs | |
|---|---|---|
| 1 | 8 | |
| 303 | 1,630 | |
| - | 5.2% | |
| 0.0 | 3.9 | |
| over 1 year ago | 17 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MuJoCo_RL_UR5
Posts with mentions or reviews of MuJoCo_RL_UR5.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-22.
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Simulating robotic arm for object manipulation
If you want to use MuJoCo, you can check this repository out.
IsaacGymEnvs
Posts with mentions or reviews of IsaacGymEnvs.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-25.
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What is the limit on parallel environments?
Although Gym/Gymnasium allows you to generate vectorized parallel environments, if you want to train in hundreds or thousands of environments you will need to use the NVIDIA simulator repertoire (Isaac Gym, Isaac Orbit or Omniverse Isaac Gym).
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How to optimize custom gym environment for GPU
Otherwise, I'd suggest checking out the Isaac Gym paper and the Isaac Gym Envs repo.
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Showing the "good" values does not help the PPO algorithm?
in the given environment (https://github.com/NVIDIA-Omniverse/IsaacGymEnvs/blob/main/isaacgymenvs/tasks/franka_cabinet.py), the task for the robot is to open a cabinet. The action values, which are the output of the agent, are the target velocity values for the robot's joints.
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Has anyone experience using/implementing "masking action" in Isaac Gym?
can it be implemented in the task-level scripts (i.e. ant.py, FrankaCabinet.py etc.) like this?
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[Material advice] Learn reinforcement leanring
IsaacGymEnvs
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Simulating robotic arm for object manipulation
And here are some reinforcment learning examples.
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What Happened to OpenAI + RL?
Gym has been great at standardizing API and providing a baseline set of environments. However, parallelizing environments with original Gym interface is cumbersome, and new simulators are being introduced with their own ways of doing things. It's not clear to me that Gym is still useful today, from a research perspective.
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[D] MuJoCo vs PyBullet? (esp. for custom environment)
If you already have experience in PyBullet then its probably not worth switching to Mujoco for creating custom environments. However, if you have the GPU compute for it, I'd recommend checking out Isaac Gym. GPU acceleration is great for spawning a bunch of envs for domain randomization, and it's already been used by recent research to get some great results that have previously taken a ridiculous amount of CPU compute.
What are some alternatives?
When comparing MuJoCo_RL_UR5 and IsaacGymEnvs you can also consider the following projects:
Unity-Robotics-Hub - Central repository for tools, tutorials, resources, and documentation for robotics simulation in Unity.
dm_control - Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
myosuite - MyoSuite is a collection of environments/tasks to be solved by musculoskeletal models simulated with the MuJoCo physics engine and wrapped in the OpenAI gym API.
robo-gym - An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
Wave-Defense-Learning-Environment - A videogame made with PyGame turned into an Open AI Gym Learning Environment for Reinforcement Learning agents.
gym3 - Vectorized interface for reinforcement learning environments