ur_openai_gym
dotmask
| ur_openai_gym | dotmask | |
|---|---|---|
| 1 | 2 | |
| 15 | 26 | |
| - | - | |
| 2.5 | 0.0 | |
| 4 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 only |
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ur_openai_gym
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How to run Machine Learning (PyTorch, Tensorflow) with ROS Melodic/Python 2.7?
I've been able to work with tensorflow 2 and Ros melodic here https://github.com/cambel/ur_openai_gym I use dockers because it makes it easy to use. Compiling your custom packages with python3 and the rest mostly works fine. I had troubles with tf but I found a work around it. Though working with docket and Ros noetic would be a better idea.
dotmask
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How to run Machine Learning (PyTorch, Tensorflow) with ROS Melodic/Python 2.7?
Do you have all your Python 3 files in a different workspace with cv_bridge? In the tutorial you shared they create a new catkin_build_ws to avoid any future problems with catkin_make? How would I compile my existing c++ files when I make changes? The steps of the tutorial seem very similar to the ones at the start of this repo: https://github.com/introlab/dotmask. In the repo, however, they use catkin_make instead of catkin build with the following arguments:
What are some alternatives?
ros-semantic-segmentation-pytorch - Pytorch implementation of Semantic Segmentation in ROS on MIT ADE20K dataset based on semantic-segmentation-pytorch by CSAIL
ur3 - ROS-based UR3/UR3e controller with simulation in Gazebo. Adaptable to other UR robots
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
Dstar-lite-pathplanner - Implementation of the D* lite algorithm in Python for "Improved Fast Replanning for Robot Navigation in Unknown Terrain"
champ - MIT Cheetah I Implementation
spot_mini_mini - Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion.
pyneat - NEAT: NeuroEvolution of Augmenting Topologies
Universal_Robots_ROS2_Description - ROS2 URDF description for Universal Robots