Skip to content

BeyondStepsRL/isaaclab-custom-direct-envs

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

5 Commits
 
 
 
 
 
 
 
 

Repository files navigation

IsaacLab Custom Direct Envs

IsaacSim IsaacLab

This repository provides examples for adding custom direct environments to IsaacLab. It includes scripts and configurations to help you set up these environments effectively. Follow these examples to extend IsaacLab's capabilities for your specific needs.

Registor envs

Easily add new environments to IsaacLab using symbolic links. This method allows access to files or directories from multiple locations without duplication, simplifying environment management.

How to Use @make_symbolic.sh

The make_symbolic.sh script creates symbolic links for all folders in the current directory. You can use this script to add new environments to the source/isaaclab_tasks/isaaclab_tasks/direct/ directory in IsaacLab.

Usage:

  1. Open a terminal and navigate to the directory containing the make_symbolic.sh script.

  2. Run the script with the following command:

    ./make_symbolic.sh /path/to/isaaclab

    Here, /path/to/isaaclab is the path to the root directory of IsaacLab. If you omit this argument, the current directory will be used as the default path.

  3. The script will create symbolic links for all folders in the current directory and add them to the source/isaaclab_tasks/isaaclab_tasks/direct/ directory.

By following this process, you can easily add new environments to IsaacLab and manage them more efficiently.

Example command

Currently, we have only created the go1 environment, and an example command for it is provided below.

python scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Velocity-Flat-Unitree-Go1-Direct-v0 --video --video_interval 500 --video_length 250 --num_envs 4096

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.3%
  • Shell 0.7%