https://github.com/thenewtoncapstone/twip-isaac-sim

Originally from: https://github.com/kevinh42/gym2real_isaacgym
https://github.com/thenewtoncapstone/twip-isaac-sim

isaac-sim ml rl

Last synced: about 2 months ago
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Originally from: https://github.com/kevinh42/gym2real_isaacgym

• Host: GitHub
• URL: https://github.com/thenewtoncapstone/twip-isaac-sim
• Owner: TheNewtonCapstone
• Created: 2024-06-02T23:58:35.000Z (7 months ago)
• Default Branch: master
• Last Pushed: 2024-11-01T19:16:10.000Z (about 2 months ago)
• Last Synced: 2024-11-01T20:20:07.714Z (about 2 months ago)
• Topics: isaac-sim, ml, rl
• Language: Python
• Homepage:
• Size: 8.96 MB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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README

        
# TWIP - Isaac Sim

This repo contains the code and configuration used during our journey of developing TWIP using NVIDIA's Isaac Sim and rl games.

## Requirements

- Ubuntu 22.04 LTS (Works with Pop OS as well)

 - Nvidia GPU with 510.73.05+ drivers (`nvidia-smi` to make sure these are set up)

- Isaac Sim (tested with version `4.0.0`)

- RL games

- Anaconda | Miniconda

## Isaac Sim Setup

- Download Isaac Sim by following the steps found within Nvidia's installation (guide)[https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_workstation.html].

- Run Isaac sim from the Omniverse application to make sure it runs properly

- Clone this repository `git clone https://github.com/TheNewtonCapstone/twip-isaac-sim`

- Remove any folder named `_isaac_sim`

- Run `cd twip-isaac-sim && ln -s home/YOUR_NAME/.local/share/ov/pkg/isaac-sim-4.0.0`

- Create the conda environment `conda env create -f environment.yml` 

## Repo Structure

 - `twip.py`: Main script used for training/testing/exporting models

- `export.py`: Script for exporting models as ONNX files

- `cfg/task/`: YAML configs for each task defining environment parameters and domain randomization

- `cfg/train/`: YAML configs for each task defining RL model and hyperparameters

- `tasks/`: Class definitions and assets for each task

- `runs/`: Model checkpoints and summaries will be saved here

## Usage

### Running Isaac Sim

The entry point of our project is `twip.py`. To run our project you must configure the environment:

- `conda activate isaac-sim`

- `source _isaac_sim/setup_conda_env.sh`

- `python twip.py --sim-only --num-envs 4`

`--sim-only` is used to run the simluation alone without any reinforcement learning. `--num-envs` specifies the number of twip environments within the simulation. To see all the options, run `twip.py --help`.

### Training

- `python train.py task={task_name}` to begin training

- Models are saved as `runs/{TaskName}/nn/{checkpoint_name}.pth`

### Exporting ONNX

 - `python twip.py --checkpoint="runs/{checkpoint_name}/nn/{task_name}.pth --export-onnx"` to export

 - Model is exported to `runs/{checkpoint_name}/nn/{task_name}.pth.onnx`

### Adding a New Task (?)

Create a new folder called `tasks/{task_name}`. The following files will be needed:

- `tasks/{task_name}/{task_name}.py`: Class defining the task. Rewards, observations, actions are all defined here.

- `tasks/{task_name}/assets/`: URDF and meshes should be placed here.

- `cfg/task/{TaskName}.yaml`: Config file defining environment parameters and domain randomization

- `cfg/train/{TaskName}PPO.yaml`: Config file defining hyperparameters for training