cyy/go2_rl_gym
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Init

Browse Source
This commit is contained in:
wty-yy
2025-12-29 16:33:09 +08:00
parent 83952bd549
commit 0c56db7e42
96 changed files with 770085 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

361
legged_gym/envs/base/legged_robot_config.py Normal file
View File

@@ -0,0 +1,361 @@
import math
from .base_config import BaseConfig
class LeggedRobotCfg(BaseConfig):
class env:
num_envs = 4096
num_observations = 48
num_privileged_obs = None # if not None a priviledge_obs_buf will be returned by step() (critic obs for assymetric training). None is returned otherwise
num_actions = 12
env_spacing = 3. # not used with heightfields/trimeshes
send_timeouts = True # send time out information to the algorithm
episode_length_s = 20 # episode length in seconds
test = False
class terrain:
mesh_type = 'trimesh' # none, plane, heightfield or trimesh
horizontal_scale = 0.1 # [m]
vertical_scale = 0.005 # [m]
border_size = 25 # [m]
curriculum = True
static_friction = 1.0
dynamic_friction = 1.0
restitution = 0.
# rough terrain only:
measure_heights = True
measured_points_x = [-0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0., 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] # 1mx1.6m rectangle (without center line)
measured_points_y = [-0.5, -0.4, -0.3, -0.2, -0.1, 0., 0.1, 0.2, 0.3, 0.4, 0.5]
selected = False # select a unique terrain type and pass all arguments
terrain_kwargs = None # Dict of arguments for selected terrain
max_init_terrain_level = 5 # starting curriculum state
terrain_length = 8.
terrain_width = 8.
num_rows= 10 # number of terrain rows (levels)
num_cols = 20 # number of terrain cols (types)
terrain_spacing = 0.5 # spacing between different terrain types [m]
# [wave, slope, rough slope, stairs down, stairs up, obstacles, stepping stones, gap, flat]
terrain_proportions = [0.1, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.0]
# trimesh only:
slope_treshold = 0.75 # slopes above this threshold will be corrected to vertical surfaces
class commands:
curriculum = False
max_curriculum = 1.
num_commands = 4 # default: lin_vel_x, lin_vel_y, ang_vel_yaw, heading (in heading mode ang_vel_yaw is recomputed from heading error)
resampling_time = 10. # time before command are changed[s]
heading_command = False # if true: compute ang vel command from heading error
zero_command_curriculum = None
# start training with zero commands and then gradually increase zero command probability
# eg. {'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
command_range_curriculum = [] # list for command range curriculums at specific training iterations
# eg: [{
# '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': [-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': [-1.5, 1.5], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # wave
{'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.5, 1.5], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # slope
{'lin_vel_x': [-1.5, 1.5], 'lin_vel_y': [-1.5, 1.5], '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': [-1.5, 1.5], 'ang_vel_yaw': [-1.5, 1.5], 'heading': [-1.57, 1.57]}, # flat
]
class ranges:
lin_vel_x = [-1.0, 1.0] # min max [m/s]
lin_vel_y = [-0.5, 0.5] # min max [m/s]
ang_vel_yaw = [-1, 1] # min max [rad/s]
heading = [-3.14, 3.14]
class init_state:
pos = [0.0, 0.0, 1.] # x,y,z [m]
rot = [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat]
lin_vel = [0.0, 0.0, 0.0] # x,y,z [m/s]
ang_vel = [0.0, 0.0, 0.0] # x,y,z [rad/s]
default_joint_angles = { # target angles when action = 0.0
"joint_a": 0.,
"joint_b": 0.}
turn_over = False # if true, initialize the robot in a flipped over position
turn_over_proportions = [0.0, 0.2, 0.8] # proportions for backflip, sideflip, no flip
turn_over_init_heights = { # initial heights range for each flip type
'backflip': [0.10, 0.15],
'sideflip': [0.16, 0.21],
}
class control:
control_type = 'P' # P: position, V: velocity, T: torques
# PD Drive parameters:
stiffness = {'joint_a': 10.0, 'joint_b': 15.} # [N*m/rad]
damping = {'joint_a': 1.0, 'joint_b': 1.5} # [N*m*s/rad]
# action scale: target angle = actionScale * action + defaultAngle
action_scale = 0.5
# decimation: Number of control action updates @ sim DT per policy DT
decimation = 4
class asset:
file = ""
name = "legged_robot" # actor name
foot_name = "None" # name of the feet bodies, used to index body state and contact force tensors
penalize_contacts_on = []
terminate_after_contacts_on = []
disable_gravity = False
collapse_fixed_joints = True # merge bodies connected by fixed joints. Specific fixed joints can be kept by adding " <... dont_collapse="true">
fix_base_link = False # fixe the base of the robot
default_dof_drive_mode = 3 # see GymDofDriveModeFlags (0 is none, 1 is pos tgt, 2 is vel tgt, 3 effort)
self_collisions = 0 # 1 to disable, 0 to enable...bitwise filter
replace_cylinder_with_capsule = True # replace collision cylinders with capsules, leads to faster/more stable simulation
flip_visual_attachments = True # Some .obj meshes must be flipped from y-up to z-up
density = 0.001
angular_damping = 0.
linear_damping = 0.
max_angular_velocity = 1000.
max_linear_velocity = 1000.
armature = 0.
thickness = 0.01
class domain_rand:
### Robot properties ###
robot_properties_update = None
# eg: {'start_iter': 5000, 'interval': 5000}
randomize_friction = True
friction_range = [0.2, 1.25]
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 = False # restitution to robot links (Robot init)
restitution_range = [0.0, 0.2]
### 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 = False # (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 = False # use last_action with 0~20 ms delay, 4 decimation
class rewards:
class scales:
termination = -0.0
tracking_lin_vel = 1.0
tracking_ang_vel = 0.5
lin_vel_z = -2.0
ang_vel_xy = -0.05
orientation = -0.
torques = -0.00001
dof_vel = -0.
dof_acc = -2.5e-7
base_height = -0.
feet_air_time = 1.0
collision = -1.
feet_stumble = -0.0
action_rate = -0.01
stand_still = -0.
class turn_over_scales:
upright = 1.0
only_positive_rewards = True # if true negative total rewards are clipped at zero (avoids early termination problems)
tracking_sigma = 0.25 # tracking reward = exp(-error^2/sigma)
soft_dof_pos_limit = 1. # percentage of urdf limits, values above this limit are penalized
soft_dof_vel_limit = 1.
soft_torque_limit = 1.
base_height_target = 1.
max_contact_force = 100. # forces above this value are penalized
curriculum_rewards = None # reward names to apply curriculum scaling to, List[dict]
# eg: [{'reward_name': 'lin_vel_z', 'start_iter': 0, 'end_iter': 1500, 'start_value': 1.0, 'end_value': 0.0}]
dynamic_sigma = None # linear interpolation of sigma based on command velocity, **Must start terrain curriculum first**
# eg: {
# "min_vel": 0.5, # min abs velocity to have default sigma
# "max_vel": 1.0, # max abs 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]
# }
turn_over_roll_threshold = math.pi / 4 # threshold on roll to use turn over rewards
min_legs_distance = 0.1 # min distance between legs to not be considered stumbling
class normalization:
class obs_scales:
lin_vel = 2.0
ang_vel = 0.25
dof_pos = 1.0
dof_vel = 0.05
height_measurements = 2.5
clip_observations = 100.
clip_actions = 100.
class noise:
add_noise = True
noise_level = 1.0 # scales other values
class noise_scales:
dof_pos = 0.01
dof_vel = 1.5
lin_vel = 0.1
ang_vel = 0.2
gravity = 0.05
height_measurements = 0.1
# viewer camera:
class viewer:
ref_env = 0
pos = [10, 0, 6] # [m]
lookat = [11., 5, 3.] # [m]
class sim:
dt = 0.005
substeps = 1
gravity = [0., 0. ,-9.81] # [m/s^2]
up_axis = 1 # 0 is y, 1 is z
class physx:
num_threads = 10
solver_type = 1 # 0: pgs, 1: tgs
num_position_iterations = 4
num_velocity_iterations = 0
contact_offset = 0.01 # [m]
rest_offset = 0.0 # [m]
bounce_threshold_velocity = 0.5 #0.5 [m/s]
max_depenetration_velocity = 1.0
max_gpu_contact_pairs = 2**23 #2**24 -> needed for 8000 envs and more
default_buffer_size_multiplier = 5
contact_collection = 2 # 0: never, 1: last sub-step, 2: all sub-steps (default=2)
class LeggedRobotCfgPPO(BaseConfig):
seed = 1
runner_class_name = 'OnPolicyRunner'
class policy:
init_noise_std = 1.0
actor_hidden_dims = [512, 256, 128]
critic_hidden_dims = [512, 256, 128]
activation = 'elu' # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid
# only for 'ActorCriticRecurrent':
# rnn_type = 'lstm'
# rnn_hidden_size = 512
# rnn_num_layers = 1
class algorithm:
# training params
value_loss_coef = 1.0
use_clipped_value_loss = True
clip_param = 0.2
entropy_coef = 0.01
num_learning_epochs = 5
num_mini_batches = 4 # mini batch size = num_envs*nsteps / nminibatches
learning_rate = 1.e-3 #5.e-4
schedule = 'adaptive' # could be adaptive, fixed
gamma = 0.99
lam = 0.95
desired_kl = 0.01
max_grad_norm = 1.
class runner:
policy_class_name = 'ActorCritic'
algorithm_class_name = 'PPO'
num_steps_per_env = 24 # per iteration
max_iterations = 1500 # number of policy updates
# logging
save_interval = 50 # check for potential saves every this many iterations
experiment_name = 'test'
run_name = ''
# load and resume
resume = False
load_run = -1 # -1 = last run
checkpoint = -1 # -1 = last saved model
resume_path = None # updated from load_run and chkpt
class LeggedRobotCfgCTS(BaseConfig):
seed = 0
runner_class_name = "OnPolicyRunnerCTS"
history_length = 5
class policy:
init_noise_std = 1.0
actor_hidden_dims = [512, 256, 128]
critic_hidden_dims = [512, 256, 128]
teacher_encoder_hidden_dims = [512, 256]
student_encoder_hidden_dims = [512, 256]
activation = 'elu' # can be elu, relu, selu, crelu, lrelu, tanh, sigmoid
latent_dim = 32
norm_type = 'l2norm' # normalization type for encoders: l2norm, simnorm
class algorithm:
# training params
value_loss_coef = 1.0
use_clipped_value_loss = True
clip_param = 0.2
entropy_coef = 0.01
num_learning_epochs = 5
num_mini_batches = 4 # mini batch size = num_envs*nsteps / nminibatches
learning_rate = 1.e-3 #5.e-4
student_encoder_learning_rate = 1e-3
schedule = 'adaptive' # could be adaptive, fixed
gamma = 0.99
lam = 0.95
desired_kl = 0.01
max_grad_norm = 1.
teacher_env_ratio = 0.75 # percentage of envs assigned to teacher
# teacher_env_ratio = 1.00 # percentage of envs assigned to teacher
class runner:
policy_class_name = 'ActorCriticCTS'
algorithm_class_name = 'CTS'
num_steps_per_env = 24 # per iteration
max_iterations = 1500 # number of policy updates
# logging
save_interval = 50 # check for potential saves every this many iterations
experiment_name = 'test'
run_name = ''
# load and resume
resume = False
load_run = -1 # -1 = last run
checkpoint = -1 # -1 = last saved model
resume_path = None # updated from load_run and chkpt
class LeggedRobotCfgMoECTS(LeggedRobotCfgCTS):
class policy(LeggedRobotCfgCTS.policy):
obs_no_goal_mask = None # mask for observation without goal inputs
student_expert_num = 8 # number of experts in the student model
class algorithm(LeggedRobotCfgCTS.algorithm):
load_balance_coef = 0.01 # coefficient for load balance loss
class runner(LeggedRobotCfgCTS.runner):
policy_class_name = 'ActorCriticMoECTS'
algorithm_class_name = 'MoECTS'