# quad_comp

**Repository Path**: wenb11/quad_comp

## Basic Information

- **Project Name**: quad_comp
- **Description**: 四足机器人控制、强化学习等算法设计
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-02-27
- **Last Updated**: 2024-02-27

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# **Compare different algorithm for quadruped**
![trot in place](https://wanghg1992.github.io/picture/fig1_trot_in_place.png)
# 1. introduction
## 1.1 classic control
- plan: body trajectory optimization(TO)
- control: optimized whole body control with null space projection(OP-NSP-WBC)
- dynamic: pinocchio
- optimal problem solver: casadi

## 1.2 reinforce learning
- algorithm: ppo
    - input: robot states
    - output: foot velocity

## 1.3 visualization
- rviz
    - urdf: robot
    - trajectory: body, foot
    - point: body, foot, zmp
    
# 2. install
## 2.1 dependency
- install anaconda or miniconda, and new environment named py37:
```shell script
- git clone git@github.com:wanghg1992/quad_comp.git --recurse-submodules
- cd quad_comp/
- conda env create -f environment.yml
```

## 2.2 install gym-quad-env
```shell script
- cd quad_comp/
- pip3 install -e simulation_environment/
```

# 3. run
## 3.1 run classic control
roscore\
./run_classic_control.sh
## 3.2 run rl train
roscore\
./run_baseline_ppo.sh
## 3.3 run visualization
roscore\
./run_visualization.sh
## 3.4 test simulation environment
roscore\
./test_simulation.sh