84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
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from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO
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class G1RoughCfg( LeggedRobotCfg ):
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class init_state( LeggedRobotCfg.init_state ):
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pos = [0.0, 0.0, 0.8] # x,y,z [m]
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default_joint_angles = { # = target angles [rad] when action = 0.0
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'left_hip_yaw_joint' : 0. ,
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'left_hip_roll_joint' : 0,
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'left_hip_pitch_joint' : -0.1,
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'left_knee_joint' : 0.3,
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'left_ankle_pitch_joint' : -0.2,
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'left_ankle_roll_joint' : 0,
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'right_hip_yaw_joint' : 0.,
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'right_hip_roll_joint' : 0,
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'right_hip_pitch_joint' : -0.1,
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'right_knee_joint' : 0.3,
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'right_ankle_pitch_joint': -0.2,
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'right_ankle_roll_joint' : 0,
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'torso_joint' : 0.
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}
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class env(LeggedRobotCfg.env):
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num_observations = 48
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num_actions = 12
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class control( LeggedRobotCfg.control ):
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# PD Drive parameters:
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control_type = 'P'
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# PD Drive parameters:
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stiffness = {'hip_yaw': 150,
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'hip_roll': 150,
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'hip_pitch': 150,
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'knee': 300,
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'ankle': 40,
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} # [N*m/rad]
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damping = { 'hip_yaw': 2,
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'hip_roll': 2,
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'hip_pitch': 2,
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'knee': 4,
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'ankle': 2,
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} # [N*m/rad] # [N*m*s/rad]
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# action scale: target angle = actionScale * action + defaultAngle
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action_scale = 0.25
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# decimation: Number of control action updates @ sim DT per policy DT
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decimation = 4
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class asset( LeggedRobotCfg.asset ):
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file = '{LEGGED_GYM_ROOT_DIR}/resources/robots/g1/urdf/g1.urdf'
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name = "g1"
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foot_name = "ankle_roll"
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penalize_contacts_on = ["hip", "knee"]
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terminate_after_contacts_on = ["torso"]
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self_collisions = 1 # 1 to disable, 0 to enable...bitwise filter
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flip_visual_attachments = False
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class rewards( LeggedRobotCfg.rewards ):
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soft_dof_pos_limit = 0.9
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base_height_target = 0.728
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class scales( LeggedRobotCfg.rewards.scales ):
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tracking_lin_vel = 1.0
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tracking_ang_vel = 0.5
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lin_vel_z = -2.0
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ang_vel_xy = -0.05
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orientation = -1.0
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base_height = -10.0
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dof_acc = -2.5e-8
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feet_air_time = 1.0
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collision = 0.0
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action_rate = -0.01
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# torques = -0.0001
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dof_pos_limits = -5.0
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class G1RoughCfgPPO( LeggedRobotCfgPPO ):
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class policy:
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init_noise_std = 0.8
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class algorithm( LeggedRobotCfgPPO.algorithm ):
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entropy_coef = 0.01
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class runner( LeggedRobotCfgPPO.runner ):
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run_name = ''
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experiment_name = 'g1'
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