isaacgym(legged_gym)学习 （一）—— 添加自己的机器人模型并训练

isaacgym(legged_gym)学习 （一）—— 添加自己的机器人模型并训练

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文章目录

• isaacgym(legged_gym)学习 （一）—— 添加自己的机器人模型并训练
• 前言
• 一、获取你机器人的urdf文件
• 二、配置GO2机器人的学习环境
• • 1.建立配置文件
  • 2.添加到task_registry
• 总结

前言

例如：isaacgym系列学习记录。

这里默认已经安装好isaacgym学习环境，并可以成功运行其中的案例

一、获取你机器人的urdf文件

这里我以宇数科技的GO2机器人为例，去其官网下载GO2的urdf文件

下载好了urdf文件，将其中resources/robots/go2文件复制到legged_gym/resources/robots/目录下

二、配置GO2机器人的学习环境

1.建立配置文件

在legged_gym/legged_gym/envs/目录下建立一个go2文件夹，然后在go2文件夹中建立一个go2_config.py的配置文件，如下：

from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO

class GO2RoughCfg( LeggedRobotCfg ):
    class init_state( LeggedRobotCfg.init_state ):
        pos = [0.0, 0.0, 0.42] # x,y,z [m]
        default_joint_angles = { # = target angles [rad] when action = 0.0
            'FL_hip_joint': 0.1,   # [rad]
            'RL_hip_joint': 0.1,   # [rad]
            'FR_hip_joint': -0.1 ,  # [rad]
            'RR_hip_joint': -0.1,   # [rad]

            'FL_thigh_joint': 0.8,     # [rad]
            'RL_thigh_joint': 1.,   # [rad]
            'FR_thigh_joint': 0.8,     # [rad]
            'RR_thigh_joint': 1.,   # [rad]

            'FL_calf_joint': -1.5,   # [rad]
            'RL_calf_joint': -1.5,    # [rad]
            'FR_calf_joint': -1.5,  # [rad]
            'RR_calf_joint': -1.5,    # [rad]
        }

    class control( LeggedRobotCfg.control ):
        # PD Drive parameters:
        control_type = 'P'
        stiffness = {'joint': 20.}  # [N*m/rad]
        damping = {'joint': 0.5}     # [N*m*s/rad]
        # action scale: target angle = actionScale * action + defaultAngle
        action_scale = 0.25
        # decimation: Number of control action updates @ sim DT per policy DT
        decimation = 4

    class asset( LeggedRobotCfg.asset ):
        file = '{LEGGED_GYM_ROOT_DIR}/resources/robots/go2/urdf/go2.urdf'
        name = "go2"
        foot_name = "foot"
        penalize_contacts_on = ["thigh", "calf"]
        terminate_after_contacts_on = ["base"]
        self_collisions = 1 # 1 to disable, 0 to enable...bitwise filter
  
    class rewards( LeggedRobotCfg.rewards ):
        soft_dof_pos_limit = 0.9
        base_height_target = 0.25
        class scales( LeggedRobotCfg.rewards.scales ):
            torques = -0.0002
            dof_pos_limits = -10.0

class GO2RoughCfgPPO( LeggedRobotCfgPPO ):
    class algorithm( LeggedRobotCfgPPO.algorithm ):
        entropy_coef = 0.01
    class runner( LeggedRobotCfgPPO.runner ):
        run_name = ''
        experiment_name = 'rough_go2'

这个配置文件，重点关注 class asset类和class runner( LeggedRobotCfgPPO.runner ):

2.添加到task_registry

from legged_gym import LEGGED_GYM_ROOT_DIR, LEGGED_GYM_ENVS_DIR
from legged_gym.envs.a1.a1_config import A1RoughCfg, A1RoughCfgPPO
from .base.legged_robot import LeggedRobot
from .anymal_c.anymal import Anymal
from .anymal_c.mixed_terrains.anymal_c_rough_config import AnymalCRoughCfg, AnymalCRoughCfgPPO
from .anymal_c.flat.anymal_c_flat_config import AnymalCFlatCfg, AnymalCFlatCfgPPO
from .anymal_b.anymal_b_config import AnymalBRoughCfg, AnymalBRoughCfgPPO
from .cassie.cassie import Cassie
from .cassie.cassie_config import CassieRoughCfg, CassieRoughCfgPPO
from .a1.a1_config import A1RoughCfg, A1RoughCfgPPO
from .go2.go2_config import GO2RoughCfg, GO2RoughCfgPPO   # 添加的

import os

from legged_gym.utils.task_registry import task_registry

task_registry.register( "anymal_c_rough", Anymal, AnymalCRoughCfg(), AnymalCRoughCfgPPO() )
task_registry.register( "anymal_c_flat", Anymal, AnymalCFlatCfg(), AnymalCFlatCfgPPO() )
task_registry.register( "anymal_b", Anymal, AnymalBRoughCfg(), AnymalBRoughCfgPPO() )
task_registry.register( "a1", LeggedRobot, A1RoughCfg(), A1RoughCfgPPO() )
task_registry.register( "cassie", Cassie, CassieRoughCfg(), CassieRoughCfgPPO() )
task_registry.register( "go2", LeggedRobot, GO2RoughCfg(), GO2RoughCfgPPO() )  # 添加的

添加上面代码中带#添加的注释的两段代码，
到此，就可以运行下面代码训练你的机器人：

python train.py --task=go2 --num_env=3000 --headless

运行下面代码，验证你训练的模型：

python play.py --task=go2

总结

提示：这里对文章进行总结：
legged_gym, 整体来说对于快速上手还是很友好的。