v0.1.8; add configs, fix bugs
This commit is contained in:
wty-yy
@@ -426,6 +426,8 @@ class LeggedRobot(BaseTask):
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Args:
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env_ids (List[int]): Environments ids for which new commands are needed
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"""
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if len(env_ids) == 0:
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return
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self.stop_heading[env_ids] = False
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# update command curriculum with train steps
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if len(self.cfg.commands.command_range_curriculum):
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@@ -443,6 +445,7 @@ class LeggedRobot(BaseTask):
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self._update_env_command_ranges()
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print(f"Command range updated at iter {current_iter}: {self.command_ranges}")
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remaining_dist = torch.clip(0.625 * self.cfg.terrain.terrain_length - torch.norm(self.commands_xy_accumulation[env_ids], dim=1) * self.cfg.commands.resampling_time, 0.0)
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self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
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if self.cfg.commands.dynamic_resample_commands:
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# arrive at boundary 0.625 times the width of the remaining distance
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if ((self.max_episode_length - self.episode_length_buf[env_ids]) == 0).any():
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@@ -472,7 +475,6 @@ class LeggedRobot(BaseTask):
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lower = self.env_command_ranges["ang_vel_yaw"][env_ids, 0]
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upper = self.env_command_ranges["ang_vel_yaw"][env_ids, 1]
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self.commands[env_ids, 2] = (upper - lower) * r + lower
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self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
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else:
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self.commands[env_ids, 0] = sample_single_interval(
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env_ids,
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@@ -859,6 +861,12 @@ class LeggedRobot(BaseTask):
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def _update_env_command_ranges(self):
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""" Update environment-wise command ranges based on current command ranges and terrain type """
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if not hasattr(self, 'terrain_ids'):
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self.env_command_ranges = {
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'lin_vel_x': torch.tensor(self.command_ranges['lin_vel_x'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
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'lin_vel_y': torch.tensor(self.command_ranges['lin_vel_y'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
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'ang_vel_yaw': torch.tensor(self.command_ranges['ang_vel_yaw'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
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'heading': torch.tensor(self.command_ranges['heading'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
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}
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return
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for terrain_id, terrain_command_ranges in enumerate(self.cfg.commands.terrain_max_command_ranges):
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env_ids = (self.terrain_ids == terrain_id).nonzero(as_tuple=False).flatten()
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@@ -1322,6 +1330,7 @@ class LeggedRobot(BaseTask):
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sigma_y = self._get_dynamic_sigma(torch.abs(self.commands[:, 1]), vmin, vmax)
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lin_vel_error_sq = torch.square(self.commands[:, :2] - self.base_lin_vel[:, :2])
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scaled_error = lin_vel_error_sq[:, 0] / sigma_x + lin_vel_error_sq[:, 1] / sigma_y
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# print(f"{self.base_lin_vel[:, :2]=}, {lin_vel_error_sq=}")
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return torch.exp(-scaled_error)
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def _reward_tracking_ang_vel(self):
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339
legged_gym/envs/go2/go2_config_fast_flat_move.py
Normal file
339
legged_gym/envs/go2/go2_config_fast_flat_move.py
Normal file
@@ -0,0 +1,339 @@
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# -*- coding: utf-8 -*-
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'''
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@File : go2_config_fast_flat_move.py
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@Time : 2026/01/10 02:31:14
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@Author : wty-yy
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@Version : 1.0
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@Blog : https://wty-yy.github.io/
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@Desc : go2 fast flat move config file
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Change command_range_curriculum, init command range
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'''
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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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mesh_type = 'plane'
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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_accumulated_xy_command = True # 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 = {'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 = 0.2 # probability of add limiting angular velocity commands when zero command is sampled
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limit_vel_prob = 0.2 # 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 = True # sample commands with low bounds
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command_range_curriculum = [ { # list for command range curriculums at specific training iterations
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'iter': 5000, # 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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}, { # list for command range curriculums at specific training iterations
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'iter': 10000, # training iteration at which the command ranges are updated
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'lin_vel_x': [-3.0, 3.0], # 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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}, { # 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': [-3.5, 3.5], # min max [m/s]
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'lin_vel_y': [-0.5, 0.5], # min max [m/s]
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'ang_vel_yaw': [-1.0, 1.0], # 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': 30000, # training iteration at which the command ranges are updated
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'lin_vel_x': [-4.0, 4.0], # min max [m/s]
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'lin_vel_y': [-0.0, 0.0], # min max [m/s]
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'ang_vel_yaw': [-0.0, 0.0], # 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': 40000, # training iteration at which the command ranges are updated
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'lin_vel_x': [-4.5, 4.5], # min max [m/s]
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'lin_vel_y': [-0.0, 0.0], # min max [m/s]
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'ang_vel_yaw': [-0.0, 0.0], # min max [rad/s]
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'heading': [-1.57, 1.57], # min max [rad]
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}
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]
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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 = [-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 = [-1.0, 1.0] # 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 = { # 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
|
||||
class turn_over_scales:
|
||||
upright = 1.0
|
||||
# dof_acc = -2.5e-7
|
||||
# dof_power = -2e-5
|
||||
# action_rate = -0.001
|
||||
# action_smoothness = -0.001
|
||||
|
||||
class noise(LeggedRobotCfg.noise):
|
||||
add_noise = True
|
||||
|
||||
class GO2CfgPPO(LeggedRobotCfgPPO):
|
||||
class algorithm(LeggedRobotCfgPPO.algorithm):
|
||||
entropy_coef = 0.01
|
||||
class runner(LeggedRobotCfgPPO.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_ppo'
|
||||
max_iterations = 100000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgCTS(LeggedRobotCfgCTS):
|
||||
class runner(LeggedRobotCfgCTS.runner):
|
||||
num_steps_per_env = 24
|
||||
run_name = ''
|
||||
experiment_name = 'go2_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class policy(LeggedRobotCfgCTS.policy):
|
||||
latent_dim = 32
|
||||
norm_type = 'l2norm'
|
||||
|
||||
class GO2CfgMoECTS(LeggedRobotCfgMoECTS):
|
||||
class policy(LeggedRobotCfgMoECTS.policy):
|
||||
obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
|
||||
student_expert_num = 8 # number of experts in the student model
|
||||
|
||||
class algorithm(LeggedRobotCfgMoECTS.algorithm):
|
||||
load_balance_coef = 0.01
|
||||
|
||||
class runner(LeggedRobotCfgMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgMCPCTS(LeggedRobotCfgMCPCTS):
|
||||
class policy(LeggedRobotCfgMCPCTS.policy):
|
||||
obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
|
||||
student_expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgMCPCTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_mcp_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgACMoECTS(LeggedRobotCfgACMoECTS):
|
||||
class policy(LeggedRobotCfgACMoECTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgACMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_ac_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgDualMoECTS(LeggedRobotCfgDualMoECTS):
|
||||
class policy(LeggedRobotCfgDualMoECTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgDualMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_dual_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgREMCTS(LeggedRobotCfgREMCTS):
|
||||
class policy(LeggedRobotCfgREMCTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgREMCTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_rem_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
@@ -1,4 +1,15 @@
|
||||
# Don't forget to change IS_HARD = False, if you want to use original training setting
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
@File : go2_config_vanilla.py
|
||||
@Time : 2026/01/10 02:26:04
|
||||
@Author : wty-yy
|
||||
@Version : 1.0
|
||||
@Blog : https://wty-yy.github.io/
|
||||
@Desc : Go2 vanilla training config
|
||||
episode length 25, resample commands 5 sec,
|
||||
open move_down_by_accumulated_xy_command, dynamic_resample_commands
|
||||
close heading_command, zero_command_curriculum, limit_vel_prob, command_range_curriculum, dynamic_sigma
|
||||
'''
|
||||
import math
|
||||
from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS, LeggedRobotCfgDualMoECTS, LeggedRobotCfgREMCTS
|
||||
|
||||
@@ -94,7 +105,7 @@ class GO2Cfg(LeggedRobotCfg):
|
||||
# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
|
||||
# terrain_proportions = [0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]
|
||||
# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
|
||||
move_down_by_accumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
|
||||
move_down_by_accumulated_xy_command = True # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
|
||||
|
||||
class commands(LeggedRobotCfg.commands):
|
||||
curriculum = False
|
||||
@@ -110,7 +121,7 @@ class GO2Cfg(LeggedRobotCfg):
|
||||
limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
|
||||
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
|
||||
stop_heading_at_limit = True # stop heading updates when vel is limited
|
||||
dynamic_resample_commands = False # sample commands with low bounds
|
||||
dynamic_resample_commands = True # sample commands with low bounds
|
||||
command_range_curriculum = []
|
||||
# command_range_curriculum = [{ # list for command range curriculums at specific training iterations
|
||||
# 'iter': 20000, # training iteration at which the command ranges are updated
|
||||
|
||||
324
legged_gym/envs/go2/go2_config_vanilla2.py
Normal file
324
legged_gym/envs/go2/go2_config_vanilla2.py
Normal file
@@ -0,0 +1,324 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
'''
|
||||
@File : go2_config_vanilla2.py
|
||||
@Time : 2026/01/10 02:27:28
|
||||
@Author : wty-yy
|
||||
@Version : 1.0
|
||||
@Blog : https://wty-yy.github.io/
|
||||
@Desc : Go2 vanilla2 training config, same as unitree rl gym except domain randomization and rewards
|
||||
episode length 20, resample commands 10 sec,
|
||||
open heading_command
|
||||
close move_down_by_accumulated_xy_command, dynamic_resample_commands, zero_command_curriculum, limit_vel_prob, command_range_curriculum, dynamic_sigma
|
||||
'''
|
||||
import math
|
||||
from legged_gym.envs.base.legged_robot_config import LeggedRobotCfg, LeggedRobotCfgPPO, LeggedRobotCfgCTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMoECTS, LeggedRobotCfgMCPCTS, LeggedRobotCfgACMoECTS, LeggedRobotCfgDualMoECTS, LeggedRobotCfgREMCTS
|
||||
|
||||
class GO2Cfg(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]
|
||||
}
|
||||
turn_over = False # initialize the robot in a flipped over position
|
||||
# turn_over_proportions = [0.1, 0.3, 0.6] # proportions for backflip, sideflip, noflip
|
||||
turn_over_proportions = [0.0, 0.2, 0.8] # proportions for backflip, sideflip, noflip
|
||||
turn_over_init_heights = { # initial heights range for each flip type
|
||||
'backflip': [0.10, 0.15],
|
||||
'sideflip': [0.16, 0.21],
|
||||
}
|
||||
# turn_over_proportions = [0.0, 1.0, 0.0] # proportions for backflip, sideflip, noflip
|
||||
|
||||
class env(LeggedRobotCfg.env):
|
||||
num_envs = 8192
|
||||
num_observations = 45
|
||||
# obs(45) + base_lin_vel(3) + height_measurements(187)
|
||||
num_privileged_obs = 45 + 3 + 4 + 12 + 12 + 187 # 263
|
||||
# num_privileged_obs = 45 + 3 + 187 # 235
|
||||
# num_privileged_obs = 48 # without height measurements
|
||||
episode_length_s = 20
|
||||
|
||||
class domain_rand(LeggedRobotCfg.domain_rand):
|
||||
### Robot properties ###
|
||||
randomize_friction = True
|
||||
friction_range = [0.0, 2.0]
|
||||
|
||||
randomize_base_mass = True
|
||||
added_mass_range = [-1., 1.]
|
||||
|
||||
randomize_link_mass = True
|
||||
multiplied_link_mass_range = [0.9, 1.1]
|
||||
|
||||
randomize_base_com = True
|
||||
added_base_com_range = [-0.03, 0.03]
|
||||
|
||||
randomize_restitution = True # restitution to robot links (Robot init)
|
||||
restitution_range = [0.0, 0.5]
|
||||
|
||||
### Environment reset ###
|
||||
randomize_pd_gains = True
|
||||
stiffness_multiplier_range = [0.9, 1.1]
|
||||
damping_multiplier_range = [0.9, 1.1]
|
||||
|
||||
randomize_motor_zero_offset = True
|
||||
motor_zero_offset_range = [-0.035, 0.035]
|
||||
|
||||
randomize_motor_strength = True # (Env reset)
|
||||
motor_strength_range = [0.8, 1.2]
|
||||
|
||||
### Environment step ###
|
||||
push_robots = True
|
||||
push_interval_s = 4
|
||||
max_push_vel_xy = 0.4
|
||||
max_push_ang_vel = 0.6
|
||||
|
||||
randomize_action_delay = True # use last_action with 0~20 ms delay, 4 decimation
|
||||
|
||||
class control(LeggedRobotCfg.control):
|
||||
# PD Drive parameters:
|
||||
control_type = 'P'
|
||||
stiffness = {'joint': 20.0} # [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 terrain(LeggedRobotCfg.terrain):
|
||||
max_init_terrain_level = 5
|
||||
# [wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
|
||||
# terrain_proportions = [0.2, 0.05, 0.05, 0.30, 0.05, 0.25, 0.0, 0.0, 0.1] # 更偏向wave
|
||||
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
|
||||
# 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
|
||||
# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
|
||||
# terrain_proportions = [0.3, 0.3, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1]
|
||||
# terrain_proportions = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]
|
||||
move_down_by_accumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
|
||||
|
||||
class commands(LeggedRobotCfg.commands):
|
||||
curriculum = False
|
||||
max_curriculum = 1.
|
||||
num_commands = 4 # default: lin_vel_x, lin_vel_y, ang_vel_yaw (in heading mode ang_vel_yaw is recomputed from heading error)
|
||||
resampling_time = 10. # time before command are changed[s]
|
||||
heading_command = True # if true: compute ang vel command from heading error
|
||||
# start training with zero commands and then gradually increase zero command probability
|
||||
zero_command_curriculum = None
|
||||
# zero_command_curriculum = {'start_iter': 0, 'end_iter': 1500, 'start_value': 0.0, 'end_value': 0.1}
|
||||
limit_ang_vel_at_zero_command_prob = 0.0 # probability of add limiting angular velocity commands when zero command is sampled
|
||||
limit_vel_prob = 0.0 # probability of limiting linear velocity command
|
||||
limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
|
||||
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
|
||||
stop_heading_at_limit = True # stop heading updates when vel is limited
|
||||
dynamic_resample_commands = False # sample commands with low bounds
|
||||
command_range_curriculum = []
|
||||
# command_range_curriculum = [{ # list for command range curriculums at specific training iterations
|
||||
# 'iter': 20000, # training iteration at which the command ranges are updated
|
||||
# 'lin_vel_x': [-1.0, 1.0], # min max [m/s]
|
||||
# 'lin_vel_y': [-1.0, 1.0], # min max [m/s]
|
||||
# 'ang_vel_yaw': [-1.5, 1.5], # min max [rad/s]
|
||||
# 'heading': [-1.57, 1.57], # min max [rad]
|
||||
# }, { # list for command range curriculums at specific training iterations
|
||||
# 'iter': 50000, # training iteration at which the command ranges are updated
|
||||
# 'lin_vel_x': [-2.0, 2.0], # min max [m/s]
|
||||
# 'lin_vel_y': [-1.0, 1.0], # min max [m/s]
|
||||
# 'ang_vel_yaw': [-2.0, 2.0], # min max [rad/s]
|
||||
# 'heading': [-1.57, 1.57], # min max [rad]
|
||||
# }]
|
||||
turn_over_zero_time = { # if turn_over is true, time robot must be stable before sampling new commands after a turn over
|
||||
"backflip": 5.0,
|
||||
"sideflip": 3.0,
|
||||
}
|
||||
# [wave, slope, rough slope, stairs up, stairs down, obstacles, stepping stones, gap, flat]
|
||||
terrain_max_command_ranges = [
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
{'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
|
||||
]
|
||||
|
||||
class ranges:
|
||||
lin_vel_x = [-2.0, 2.0] # min max [m/s]
|
||||
lin_vel_y = [-1.0, 1.0] # min max [m/s]
|
||||
ang_vel_yaw = [-2.0, 2.0] # min max [rad/s]
|
||||
heading = [-1.57, 1.57] # min max [rad]
|
||||
|
||||
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 = 0.38
|
||||
only_positive_rewards = False
|
||||
max_contact_force = 147. # forces above this value are penalized, go2 weight 15kg
|
||||
curriculum_rewards = [
|
||||
{'reward_name': 'lin_vel_z', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
|
||||
{'reward_name': 'correct_base_height', 'start_iter': 0, 'end_iter': 5000, 'start_value': 1.0, 'end_value': 10.0},
|
||||
# {'reward_name': 'dof_power', 'start_iter': 0, 'end_iter': 3000, 'start_value': 1.0, 'end_value': 0.1},
|
||||
# {'reward_name': 'upright', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0},
|
||||
]
|
||||
tracking_sigma = 0.25 # tracking reward = exp(-error^2/sigma)
|
||||
dynamic_sigma = None
|
||||
# dynamic_sigma = { # linear interpolation of sigma based on command velocity, **Must start terrain curriculum first**
|
||||
# "min_lin_vel": 0.5, # min abs linear velocity to have default sigma
|
||||
# "max_lin_vel": 1.5, # max abs linear velocity to have max sigma
|
||||
# "min_ang_vel": 1.0, # min abs angular velocity to have default sigma
|
||||
# "max_ang_vel": 2.0, # max abs angular velocity to have max sigma
|
||||
# # wave, slope, rough_slope, stairs up, stairs down, obstacles, stepping_stones, gap, flat]
|
||||
# # "max_sigma": [1/3, 1/4, 1/4, 1/2.7, 1/2.7, 1/2, 1, 1, 1/4]
|
||||
# "max_sigma": [5/12, 1/4, 1/4, 1/2, 1/2, 3/4, 1, 1, 1/4]
|
||||
# }
|
||||
min_legs_distance = 0.1 # min distance between legs to not be considered stumbling
|
||||
class scales:
|
||||
# tracking_lin_vel = 1.0
|
||||
# tracking_ang_vel = 0.2
|
||||
# lin_vel_z = -10.0
|
||||
# base_height = -50.0
|
||||
# action_rate = -0.005
|
||||
# similar_to_default = -0.1
|
||||
# dof_power = -1e-3 # 能够明显抑制跳跃
|
||||
# dof_acc = -3e-7
|
||||
|
||||
# tracking_lin_vel = 1.0
|
||||
# tracking_ang_vel = 0.5
|
||||
# lin_vel_z = -2.0
|
||||
# ang_vel_xy = -0.05
|
||||
# dof_acc = -2.5e-7
|
||||
# dof_power = -1e-3 # 能够明显抑制跳跃
|
||||
# # torques = -1e-4 # 无用会走着走着倒了
|
||||
# correct_base_height = -10.0
|
||||
# action_rate = -0.01
|
||||
# action_smoothness = -0.01
|
||||
# collision = -1.0
|
||||
# dof_pos_limits = -2.0
|
||||
# feet_regulation = -0.05
|
||||
# hip_to_default = -0.1
|
||||
# similar_to_default = -0.05
|
||||
|
||||
# CTS reward
|
||||
tracking_lin_vel = 1.0
|
||||
tracking_ang_vel = 0.5
|
||||
lin_vel_z = -2.0
|
||||
ang_vel_xy = -0.05
|
||||
dof_acc = -2.5e-7
|
||||
dof_power = -2e-5
|
||||
torques = -1e-4
|
||||
correct_base_height = -1.0
|
||||
action_rate = -0.01
|
||||
action_smoothness = -0.01
|
||||
collision = -1.0
|
||||
dof_pos_limits = -2.0
|
||||
feet_regulation = -0.05
|
||||
# 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
|
||||
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)
|
||||
# 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
|
||||
# similar_to_default = -0.01
|
||||
# feet_contact_forces = -1.0 # 尝试加入但并没有起到任何效果, 删除
|
||||
|
||||
turn_over_roll_threshold = math.pi / 4 # threshold on roll to use turn over rewards
|
||||
class turn_over_scales:
|
||||
upright = 1.0
|
||||
# dof_acc = -2.5e-7
|
||||
# dof_power = -2e-5
|
||||
# action_rate = -0.001
|
||||
# action_smoothness = -0.001
|
||||
|
||||
class noise(LeggedRobotCfg.noise):
|
||||
add_noise = True
|
||||
|
||||
class GO2CfgPPO(LeggedRobotCfgPPO):
|
||||
class algorithm(LeggedRobotCfgPPO.algorithm):
|
||||
entropy_coef = 0.01
|
||||
class runner(LeggedRobotCfgPPO.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_ppo'
|
||||
max_iterations = 100000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgCTS(LeggedRobotCfgCTS):
|
||||
class runner(LeggedRobotCfgCTS.runner):
|
||||
num_steps_per_env = 24
|
||||
run_name = ''
|
||||
experiment_name = 'go2_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class policy(LeggedRobotCfgCTS.policy):
|
||||
latent_dim = 32
|
||||
norm_type = 'l2norm'
|
||||
|
||||
class GO2CfgMoECTS(LeggedRobotCfgMoECTS):
|
||||
class policy(LeggedRobotCfgMoECTS.policy):
|
||||
obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
|
||||
student_expert_num = 8 # number of experts in the student model
|
||||
|
||||
class algorithm(LeggedRobotCfgMoECTS.algorithm):
|
||||
load_balance_coef = 0.01
|
||||
|
||||
class runner(LeggedRobotCfgMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgMCPCTS(LeggedRobotCfgMCPCTS):
|
||||
class policy(LeggedRobotCfgMCPCTS.policy):
|
||||
obs_no_goal_mask = [True] * 6 + [False] * 3 + [True] * 36 # mask for obs without command info
|
||||
student_expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgMCPCTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_mcp_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgACMoECTS(LeggedRobotCfgACMoECTS):
|
||||
class policy(LeggedRobotCfgACMoECTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgACMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_ac_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgDualMoECTS(LeggedRobotCfgDualMoECTS):
|
||||
class policy(LeggedRobotCfgDualMoECTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgDualMoECTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_dual_moe_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
|
||||
class GO2CfgREMCTS(LeggedRobotCfgREMCTS):
|
||||
class policy(LeggedRobotCfgREMCTS.policy):
|
||||
expert_num = 8 # number of experts in the student model
|
||||
|
||||
class runner(LeggedRobotCfgREMCTS.runner):
|
||||
run_name = ''
|
||||
experiment_name = 'go2_rem_cts'
|
||||
max_iterations = 150000
|
||||
save_interval = 500
|
||||
@@ -210,10 +210,13 @@ class _OnnxPolicyExporter(torch.nn.Module):
|
||||
|
||||
elif hasattr(policy, "student_moe_encoder"):
|
||||
self.student_moe_encoder = copy.deepcopy(policy.student_moe_encoder)
|
||||
self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
|
||||
self.history_length = policy.history.shape[1]
|
||||
self.forward = self.forward_moe_cts
|
||||
self.input_dim = self.history_length * policy.history.shape[2]
|
||||
if hasattr(policy, "obs_no_goal_mask"):
|
||||
self.obs_no_goal_mask = copy.deepcopy(policy.obs_no_goal_mask).cpu()
|
||||
else:
|
||||
self.forward = self.forward_rem_cts
|
||||
|
||||
else: # PPO
|
||||
self.forward = self.forward_ppo
|
||||
@@ -286,6 +289,17 @@ class _OnnxPolicyExporter(torch.nn.Module):
|
||||
x = torch.cat([latent, last_obs], dim=1)
|
||||
|
||||
return self.actor(x), weights, latent
|
||||
|
||||
def forward_rem_cts(self, x):
|
||||
x = self.normalizer(x)
|
||||
history, obs_dim = self.flatten_obs(x)
|
||||
|
||||
last_obs = history[:, -obs_dim:]
|
||||
|
||||
latent, weights = self.student_moe_encoder(history)
|
||||
x = torch.cat([latent, last_obs], dim=1)
|
||||
|
||||
return self.actor(x), weights, latent
|
||||
|
||||
def forward_mcp_cts(self, x):
|
||||
x = self.normalizer(x)
|
||||
|
||||
Reference in New Issue
Block a user