https://github.com/enactic/openarm_maniskill_simulation
OpenArm Maniskill Simulation
https://github.com/enactic/openarm_maniskill_simulation
Last synced: 5 months ago
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OpenArm Maniskill Simulation
- Host: GitHub
- URL: https://github.com/enactic/openarm_maniskill_simulation
- Owner: enactic
- Created: 2025-07-28T07:29:15.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-02T10:59:54.000Z (11 months ago)
- Last Synced: 2025-08-02T12:34:52.431Z (11 months ago)
- Language: Python
- Size: 16.8 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Installation Guide
### Specification
This repository has been tested on Ubuntu 22.04.
We do not guarantee that it will work on other platforms.
### Install ManiSkill
The required versions are:
* **ManiSKill v3.0.0b21**
From this point on, it is assumed that you have created a virtual environment named `env_maniskill` using miniconda or anaconda and will be working within that environment.
Please refer to the [official](https://maniskill.readthedocs.io/en/latest/user_guide/getting_started/installation.html) page for the installation method of ManiSkill.
Here, we provide a sample installation procedure.
1. Create virtual environment
```bash
conda create -n env_maniskill python=3.11
```
2. Activate your virtual env
```bash
conda activate env_maniskill
```
3. Install libvulkan
```bash
sudo apt-get install libvulkan1
```
4. Clone Maniskill git at your HOME directory
```bash
cd ~
git clone https://github.com/haosulab/ManiSkill.git
```
5. Install Maniskill
```bash
cd ManiSkill && pip install -e .
pip install torch torchvision torchaudio tensorboard wandb
```
6. Install OpenArm pick up task
```bash
cd ~
git clone https://github.com/enactic/openarm_maniskill_simulation.git
# copy openarm assets
cp -r ~/openarm_maniskill_simulation/urdf ~/ManiSkill/mani_skill/assets/robots/openarm
# copy agents(robot)
cp ~/openarm_maniskill_simulation/mani_skill/agents/robots/__init__.py ~/ManiSkill/mani_skill/agents/robots
cp -r ~/openarm_maniskill_simulation/mani_skill/agents/robots/openarm ~/ManiSkill/mani_skill/agents/robots
# copy task files
cp ~/openarm_maniskill_simulation//mani_skill/envs/tasks/tabletop/pick_cube.py ~/ManiSkill/mani_skill/envs/tasks/tabletop
cp ~/openarm_maniskill_simulation//mani_skill/envs/tasks/tabletop/pick_cube_cfgs.py ~/ManiSkill/mani_skill/envs/tasks/tabletop
cp ~/openarm_maniskill_simulation//mani_skill/envs/tasks/tabletop/pull_cube.py ~/ManiSkill/mani_skill/envs/tasks/tabletop
cp ~/openarm_maniskill_simulation//mani_skill/envs/tasks/tabletop/push_cube.py ~/ManiSkill/mani_skill/envs/tasks/tabletop
```
### Run Training
You can run different tasks.
Replace with one of the following available tasks:
| Task Description | Task Name | Demo |
| ----------------------- | ------------------------------ | ----------------------------------- |
| Push the cube to the center of the target. | `PushCube-v1` | 
|
| Pull the cube to the cente of the target. | `PullCube-v1` | 
|
| Pick up the cube and move it to the target position. | `PickCube-v1` | 
|
```bash
cd ~/ManiSkill/examples/baselines/ppo/
python ppo.py \
--env_id= \
--exp-name= \
--num_envs=1024 \
--total_timesteps=12_000_000 \
--eval_freq=10 \
--num-steps=20 \
--num_eval_envs 8
```
If you want to manage training logs with [Weights & Biases](https://wandb.ai/site/), please run the following.
```bash
export WANDB_API_KEY=`your wandb api key`
cd ~/ManiSkill/examples/baselines/ppo/
python ppo.py \
--env_id= \
--exp-name= \
--num_envs=1024 \
--total_timesteps=12_000_000 \
--eval_freq=10 \
--num-steps=20 \
--track \
--wandb_project_name `your wandb project name` \
--exp-name `your wandb experiment name` \
--num_eval_envs 8
```
### Run evaluate
How to evaluate a trained model.
```bash
cd ~/ManiSkill/examples/baselines/ppo/
python ppo.py \
--env_id= \
--evaluate \
--checkpoint `your training model path`
--exp-name= \
--num_eval_envs=1
```
```
Running evaluation
####
args.num_iterations=390 args.num_envs=512 args.num_eval_envs=1
args.minibatch_size=800 args.batch_size=25600 args.update_epochs=4
####
Epoch: 1, global_step=0
Evaluating
Evaluated 50 steps resulting in 1 episodes
eval_success_once_mean=1.0
eval_return_mean=39.73570251464844
eval_episode_len_mean=50.0
eval_reward_mean=0.7947140336036682
eval_success_at_end_mean=1.0
```