SE2-equivariant-grasp-learning

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SE2-equivariant-grasp-learning

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Code for the paper Sample Efficient Grasp Learning Using Equivariant Models

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# On Robot Grasp Learning Using Equivariant Models
# Sample Efficient Grasp Learning Using Equivariant Models

**Abstract**

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses
in SE(2). In this paper, we recognize that the optimal grasp function is SE(2)-equivariant and can be modeled
using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample
efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This
is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.

<h3>
<center>
<a href="https://arxiv.org/abs/2202.09468">Paper</a> &emsp;&emsp;&emsp;
<a href="https://zxp-s-works.github.io/equivariant_grasp_site/">Website</a> &emsp;&emsp;&emsp;
<a href="https://www.youtube.com/watch?v=au59crsgiKw">Video</a>
</center>
</h3>

<h3>
    <center>
        <a href="https://github.com/ZXP-S-works/SE2-equivariant-grasp-learning/tree/main">Minimum Code*</a> &emsp;&emsp;&emsp;
        <a href="https://github.com/ZXP-S-works/SE2-equivariant-grasp-learning/tree/complete_code">Complete Code*</a> &emsp;&emsp;&emsp;
    </center>
</h3>

*Note that the minimum code only includes our method while the complete code includes all baselines in our paper.

**Citation**
```
@article{zhu2023grasp,
  title={On Robot Grasp Learning Using Equivariant Models},
  author={Zhu, Xupeng and Wang, Dian and Su, Guanang and Biza, Ondrej and Walters, Robin and Platt, Robert },
  journal={Autonomous Robots},
  year={2023} }
```
```
@article{zhu2022grasp,
  title={Sample Efficient Grasp Learning Using Equivariant Models},
  author={Zhu, Xupeng and Wang, Dian and Biza, Ondrej and Su, Guanang and Walters, Robin and Platt, Robert},
  journal={Proceedings of Robotics: Science and Systems (RSS)},
  year={2022} }
```

## Environments

### Simulation Environment

<table border="0">
 <tr>
    <td>The simulation environment is random_household_picking_clutter_full_obs_30. This environment is implemented in /helping_hands_rl_envs/envs/pybullet_envs.
</td>
    <td><p align="center">
<img width="180%" src="./images/simulation_env.png">
</p></td>
 </tr>
</table>

### Physical Environment

<table border="0">
 <tr>
    <td>The physical robot environment is DualBinFrontRear. To train on this environment, a physical robot set up is required.
</td>
    <td><p align="center">
<img width="150%" src="./images/UR5_setup.png">
</p></td>
 </tr>
</table>

## Installation

You can install the required packages either through `Option1: anaconda` or through `Option2: pip`.

### Option1: anaconda
1. Install [anaconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/)
1. Create and activate a conda virtual environment with python3.7.
    ```
    sudo apt update
    conda create -n eqvar_grasp python=3.7
    conda activate eqvar_grasp
    ```
1. Download the git repository.
    ```
    git clone https://github.com/ZXP-S-works/SE2-equivariant-grasp-learning.git
    cd SE2-equivariant-grasp-learning
    ```
1. Install [PyTorch](https://pytorch.org/) (Recommended: pytorch==1.8.1, torchvision==0.9.1)
1. Install [CuPy](https://github.com/cupy/cupy)
    ```
    conda install -c conda-forge cupy
    ```
1. Install other requirement packages
    ```
    pip install -r requirements.txt
    ```
1. Clone and install the environment repo 
    ```
    git clone https://github.com/ColinKohler/helping_hands_rl_envs.git -b xupeng_realistic
    cd helping_hands_rl_envs
    pip install -r requirements.txt
    cd ..
    ```
1. Go to the scripts folder of this repo to run experiments
    ```
    cd asrse3/scripts
    ```

### Option2: pip
1. Install python3.7
1. Download the git repository.
    ```
    git clone https://github.com/ZXP-S-works/SE2-equivariant-grasp-learning.git
    cd SE2-equivariant-grasp-learning
    ```
1. Install [PyTorch](https://pytorch.org/) (Recommended: pytorch==1.8.1, torchvision==0.9.1)
1. Install [CuPy](https://github.com/cupy/cupy)
1. Install other requirement packages
    ```
    pip install -r requirements.txt
    ```
1. Clone and install the environment repo 
    ```
    git clone https://github.com/ColinKohler/helping_hands_rl_envs.git -b xupeng_realistic
    cd helping_hands_rl_envs
    pip install -r requirements.txt
    cd ..
    ```
1. Go to the scripts folder of this repo to run experiments
    ```
    cd asrse3/scripts
    ```

## Reinforcement learning

### Training baselines in simulation

Our method
```bash
python3 ./scripts/main.py 
```

To visualize the simulation and the policy learning, set --render=t.

To load the trained model and visualize the learned policy, you can run the following code:
```bash
python3 ./scripts/main.py
--log_pre="PATH_TO_SAVE_THE_LOG"
--step_eps=0
--init_eps=0
--render=t
--train_tau=0.002
--training_offset=10000
--load_model_pre="PATH_TO_THE_MODEL"
--ADITTIONAL_PARAMETERS_FOR_YOUR_MODEL
```
Where the ```"PATH_TO_THE_MODEL"``` is the path to the trained model, without ```_qx.pt```. For example 
```--load_model_pre="/results/household_repo/snapshot_random_household_picking_clutter_full_obs"```.

In addition, ```ADITTIONAL_PARAMETERS_FOR_YOUR_MODEL``` should be set correspondingly, for example ```--model, --alg, 
--action_selection```.

### Real-time training in a physical robot
The parallel training is only implemented in physical robot environment (code for physical robot environment is coming 
soon). However, one can easily modify it to any environment.

```
python3 ./scripts/train_robot_parallel.py --env=DualBinFrontRear --hm_threshold=0.015 --step_eps=20 --init_eps=1. --final_eps=0.
```

<table border="0">
 <tr>
    <td>The right figure illustrates the parallel training.
</td>
    <td><p align="center">
<img width="70%" src="./images/dualbin_paralle_run.png">
</p></td>
 </tr>
</table>

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