v0.1.6; add move_down_by_acuumulated_xy_command, dynamic_resample_commands optional
This commit is contained in:
wty-yy
313
legged_gym/envs/go2/go2_config_vanilla.py
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313
legged_gym/envs/go2/go2_config_vanilla.py
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# Don't forget to change IS_HARD = False, if you want to use original training setting
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import math
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from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS, LeggedRobotCfgDualMoECTS, LeggedRobotCfgREMCTS
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class GO2Cfg(LeggedRobotCfg):
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class init_state(LeggedRobotCfg.init_state):
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pos = [0.0, 0.0, 0.42] # x,y,z [m]
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default_joint_angles = { # = target angles [rad] when action = 0.0
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'FL_hip_joint': 0.1, # [rad]
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'RL_hip_joint': 0.1, # [rad]
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'FR_hip_joint': -0.1 , # [rad]
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'RR_hip_joint': -0.1, # [rad]
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'FL_thigh_joint': 0.8, # [rad]
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'RL_thigh_joint': 1., # [rad]
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'FR_thigh_joint': 0.8, # [rad]
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'RR_thigh_joint': 1., # [rad]
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'FL_calf_joint': -1.5, # [rad]
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'RL_calf_joint': -1.5, # [rad]
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'FR_calf_joint': -1.5, # [rad]
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'RR_calf_joint': -1.5, # [rad]
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}
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turn_over = False # initialize the robot in a flipped over position
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# turn_over_proportions = [0.1, 0.3, 0.6] # proportions for backflip, sideflip, noflip
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turn_over_proportions = [0.0, 0.2, 0.8] # proportions for backflip, sideflip, noflip
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turn_over_init_heights = { # initial heights range for each flip type
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'backflip': [0.10, 0.15],
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'sideflip': [0.16, 0.21],
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}
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# turn_over_proportions = [0.0, 1.0, 0.0] # proportions for backflip, sideflip, noflip
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class env(LeggedRobotCfg.env):
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num_envs = 8192
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num_observations = 45
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# obs(45) + base_lin_vel(3) + height_measurements(187)
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num_privileged_obs = 45 + 3 + 4 + 12 + 12 + 187 # 263
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# num_privileged_obs = 45 + 3 + 187 # 235
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# num_privileged_obs = 48 # without height measurements
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episode_length_s = 25
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class domain_rand(LeggedRobotCfg.domain_rand):
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### Robot properties ###
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randomize_friction = True
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friction_range = [0.0, 2.0]
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randomize_base_mass = True
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added_mass_range = [-1., 1.]
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randomize_link_mass = True
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multiplied_link_mass_range = [0.9, 1.1]
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randomize_base_com = True
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added_base_com_range = [-0.03, 0.03]
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randomize_restitution = True # restitution to robot links (Robot init)
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restitution_range = [0.0, 0.5]
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### Environment reset ###
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randomize_pd_gains = True
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stiffness_multiplier_range = [0.9, 1.1]
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damping_multiplier_range = [0.9, 1.1]
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randomize_motor_zero_offset = True
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motor_zero_offset_range = [-0.035, 0.035]
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randomize_motor_strength = True # (Env reset)
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motor_strength_range = [0.8, 1.2]
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### Environment step ###
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push_robots = True
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push_interval_s = 4
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max_push_vel_xy = 0.4
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max_push_ang_vel = 0.6
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randomize_action_delay = True # use last_action with 0~20 ms delay, 4 decimation
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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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stiffness = {'joint': 20.0} # [N*m/rad]
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damping = {'joint': 0.5} # [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 terrain(LeggedRobotCfg.terrain):
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max_init_terrain_level = 5
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# [wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
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# terrain_proportions = [0.2, 0.05, 0.05, 0.30, 0.05, 0.25, 0.0, 0.0, 0.1] # 更偏向wave
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terrain_proportions = [0.05, 0.20, 0.05, 0.25, 0.10, 0.20, 0.0, 0.0, 0.15] # 这个更偏向平地斜坡
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# terrain_proportions = [0.20, 0.05, 0.05, 0.30, 0.15, 0.20, 0.0, 0.0, 0.05] # 更偏向wave和stairs
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# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
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# terrain_proportions = [0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]
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# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
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move_down_by_acuumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
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class commands(LeggedRobotCfg.commands):
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curriculum = False
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max_curriculum = 1.
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num_commands = 4 # default: lin_vel_x, lin_vel_y, ang_vel_yaw (in heading mode ang_vel_yaw is recomputed from heading error)
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resampling_time = 5. # time before command are changed[s]
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heading_command = False # if true: compute ang vel command from heading error
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# start training with zero commands and then gradually increase zero command probability
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zero_command_curriculum = None
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# zero_command_curriculum = {'start_iter': 0, 'end_iter': 1500, 'start_value': 0.0, 'end_value': 0.1}
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limit_ang_vel_at_zero_command_prob = 0.0 # probability of add limiting angular velocity commands when zero command is sampled
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limit_vel_prob = 0.0 # probability of limiting linear velocity command
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limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
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limit_vel = {"lin_vel_x": [-1, 1], "lin_vel_y": [-1, 1], "ang_vel_yaw": [-1, 0, 1]} # sample vel commands from min [-1] or zero [0] or max [1] range only
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stop_heading_at_limit = True # stop heading updates when vel is limited
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dynamic_resample_commands = False # sample commands with low bounds
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command_range_curriculum = []
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# command_range_curriculum = [{ # list for command range curriculums at specific training iterations
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# 'iter': 20000, # training iteration at which the command ranges are updated
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# 'lin_vel_x': [-1.0, 1.0], # min max [m/s]
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# 'lin_vel_y': [-1.0, 1.0], # min max [m/s]
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# 'ang_vel_yaw': [-1.5, 1.5], # min max [rad/s]
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# 'heading': [-1.57, 1.57], # min max [rad]
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# }, { # list for command range curriculums at specific training iterations
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# 'iter': 50000, # training iteration at which the command ranges are updated
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# 'lin_vel_x': [-2.0, 2.0], # min max [m/s]
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# 'lin_vel_y': [-1.0, 1.0], # min max [m/s]
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# 'ang_vel_yaw': [-2.0, 2.0], # min max [rad/s]
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# 'heading': [-1.57, 1.57], # min max [rad]
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# }]
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turn_over_zero_time = { # if turn_over is true, time robot must be stable before sampling new commands after a turn over
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"backflip": 5.0,
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"sideflip": 3.0,
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}
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# [wave, slope, rough slope, stairs up, stairs down, obstacles, stepping stones, gap, flat]
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terrain_max_command_ranges = [
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{'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # wave
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{'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # slope
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{'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # rough slope
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{'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # stairs up
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{'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # stairs down
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{'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # obstacles
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{'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # stepping stones
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{'lin_vel_x': [-1.0, 1.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # gap
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{'lin_vel_x': [-2.0, 2.0], 'lin_vel_y': [-1.0, 1.0], 'ang_vel_yaw': [-2.0, 2.0], 'heading': [-1.57, 1.57]}, # flat
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]
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class ranges:
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lin_vel_x = [-2.0, 2.0] # min max [m/s]
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lin_vel_y = [-1.0, 1.0] # min max [m/s]
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ang_vel_yaw = [-2.0, 2.0] # min max [rad/s]
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heading = [-1.57, 1.57] # min max [rad]
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class asset(LeggedRobotCfg.asset):
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file = '{LEGGED_GYM_ROOT_DIR}/resources/robots/go2/urdf/go2.urdf'
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name = "go2"
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foot_name = "foot"
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penalize_contacts_on = ["thigh", "calf"]
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terminate_after_contacts_on = ["base"]
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self_collisions = 1 # 1 to disable, 0 to enable...bitwise filter
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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 = 0.38
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only_positive_rewards = False
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max_contact_force = 147. # forces above this value are penalized, go2 weight 15kg
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curriculum_rewards = [
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{'reward_name': 'lin_vel_z', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
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{'reward_name': 'correct_base_height', 'start_iter': 0, 'end_iter': 5000, 'start_value': 1.0, 'end_value': 10.0},
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# {'reward_name': 'dof_power', 'start_iter': 0, 'end_iter': 3000, 'start_value': 1.0, 'end_value': 0.1},
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# {'reward_name': 'upright', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
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]
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tracking_sigma = 0.25 # tracking reward = exp(-error^2/sigma)
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dynamic_sigma = None
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# dynamic_sigma = { # linear interpolation of sigma based on command velocity, **Must start terrain curriculum first**
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# "min_lin_vel": 0.5, # min abs linear velocity to have default sigma
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# "max_lin_vel": 1.5, # max abs linear velocity to have max sigma
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# "min_ang_vel": 1.0, # min abs angular velocity to have default sigma
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# "max_ang_vel": 2.0, # max abs angular velocity to have max sigma
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# # wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
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# # "max_sigma": [1/3, 1/4, 1/4, 1/2.7, 1/2.7, 1/2, 1, 1, 1/4]
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# "max_sigma": [5/12, 1/4, 1/4, 1/2, 1/2, 3/4, 1, 1, 1/4]
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# }
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min_legs_distance = 0.1 # min distance between legs to not be considered stumbling
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class scales:
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# tracking_lin_vel = 1.0
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# tracking_ang_vel = 0.2
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# lin_vel_z = -10.0
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# base_height = -50.0
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# action_rate = -0.005
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# similar_to_default = -0.1
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# dof_power = -1e-3 # 能够明显抑制跳跃
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# dof_acc = -3e-7
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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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# dof_acc = -2.5e-7
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# dof_power = -1e-3 # 能够明显抑制跳跃
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# # torques = -1e-4 # 无用会走着走着倒了
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# correct_base_height = -10.0
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# action_rate = -0.01
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# action_smoothness = -0.01
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# collision = -1.0
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# dof_pos_limits = -2.0
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# feet_regulation = -0.05
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# hip_to_default = -0.1
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# similar_to_default = -0.05
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# CTS reward
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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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dof_acc = -2.5e-7
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dof_power = -2e-5
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torques = -1e-4
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correct_base_height = -1.0
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action_rate = -0.01
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action_smoothness = -0.01
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collision = -1.0
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dof_pos_limits = -2.0
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feet_regulation = -0.05
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# CTS奖励训出来双脚距离非常近, 真机效果很差, 但是sim2sim能上20cm楼梯, 尝试加入hip_to_default奖励或similar_to_default奖励
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hip_to_default = -0.05 # 在训练到y=1.5时, 双脚会明显碰撞, 为避免该问题提升hip, 效果更差, 还是保持0.05 (y最大也只到0.1了)
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# legs_distance = -1.5 # 奖励双脚距离, 避免CTS训练出来双脚距离过近, 尝试加入后robogauge flat验证效果变差, 删除
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# similar_to_default = -0.01
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# feet_contact_forces = -1.0 # 尝试加入但并没有起到任何效果, 删除
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turn_over_roll_threshold = math.pi / 4 # threshold on roll to use turn over rewards
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class turn_over_scales:
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upright = 1.0
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# dof_acc = -2.5e-7
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# dof_power = -2e-5
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# action_rate = -0.001
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# action_smoothness = -0.001
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class noise(LeggedRobotCfg.noise):
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add_noise = True
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class GO2CfgPPO(LeggedRobotCfgPPO):
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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 = 'go2_ppo'
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max_iterations = 100000
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save_interval = 500
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class GO2CfgCTS(LeggedRobotCfgCTS):
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class runner(LeggedRobotCfgCTS.runner):
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num_steps_per_env = 24
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run_name = ''
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experiment_name = 'go2_cts'
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max_iterations = 150000
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save_interval = 500
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class policy(LeggedRobotCfgCTS.policy):
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latent_dim = 32
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norm_type = 'l2norm'
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class GO2CfgMoECTS(LeggedRobotCfgMoECTS):
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class policy(LeggedRobotCfgMoECTS.policy):
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obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
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student_expert_num = 8 # number of experts in the student model
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class algorithm(LeggedRobotCfgMoECTS.algorithm):
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load_balance_coef = 0.01
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class runner(LeggedRobotCfgMoECTS.runner):
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run_name = ''
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experiment_name = 'go2_moe_cts'
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max_iterations = 150000
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save_interval = 500
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class GO2CfgMCPCTS(LeggedRobotCfgMCPCTS):
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class policy(LeggedRobotCfgMCPCTS.policy):
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obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
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student_expert_num = 8 # number of experts in the student model
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class runner(LeggedRobotCfgMCPCTS.runner):
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run_name = ''
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experiment_name = 'go2_mcp_cts'
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max_iterations = 150000
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save_interval = 500
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class GO2CfgACMoECTS(LeggedRobotCfgACMoECTS):
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class policy(LeggedRobotCfgACMoECTS.policy):
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expert_num = 8 # number of experts in the student model
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class runner(LeggedRobotCfgACMoECTS.runner):
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run_name = ''
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experiment_name = 'go2_ac_moe_cts'
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max_iterations = 150000
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save_interval = 500
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class GO2CfgDualMoECTS(LeggedRobotCfgDualMoECTS):
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class policy(LeggedRobotCfgDualMoECTS.policy):
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expert_num = 8 # number of experts in the student model
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class runner(LeggedRobotCfgDualMoECTS.runner):
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run_name = ''
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experiment_name = 'go2_dual_moe_cts'
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max_iterations = 150000
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save_interval = 500
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class GO2CfgREMCTS(LeggedRobotCfgREMCTS):
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class policy(LeggedRobotCfgREMCTS.policy):
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expert_num = 8 # number of experts in the student model
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class runner(LeggedRobotCfgREMCTS.runner):
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run_name = ''
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experiment_name = 'go2_rem_cts'
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max_iterations = 150000
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save_interval = 500
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