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config changed

This commit is contained in:
Jerry Xu 2024-05-15 21:29:54 -04:00
parent 96317ac12f
commit 16a26a47ab
12 changed files with 335 additions and 58 deletions
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2
legged_gym/legged_gym/envs/a1/a1_crawl_config.py
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@ -1,5 +1,5 @@
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1CrawlCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/a1/a1_down_config.py
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@ -1,6 +1,6 @@
from os import path as osp
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1DownCfg( A1FieldCfg ):

59
legged_gym/legged_gym/envs/a1/a1_field_config.py
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@ -1,5 +1,4 @@
import numpy as np
import os.path as osp
from legged_gym.envs.a1.a1_config import A1RoughCfg, A1RoughCfgPPO
class A1FieldCfg( A1RoughCfg ):
@ -60,7 +59,7 @@ class A1FieldCfg( A1RoughCfg ):
BarrierTrack_kwargs = dict(
options= [
# "jump",
# "climb",
# "crawl",
# "tilt",
# "leap",
@ -69,11 +68,11 @@ class A1FieldCfg( A1RoughCfg ):
track_block_length= 2., # the x-axis distance from the env origin point
wall_thickness= (0.04, 0.2), # [m]
wall_height= -0.05,
jump= dict(
climb= dict(
height= (0.2, 0.6),
depth= (0.1, 0.8), # size along the forward axis
fake_offset= 0.0, # [m] an offset that make the robot easier to get into the obstacle
jump_down_prob= 0., # probability of jumping down use it in non-virtual terrain
climb_down_prob= 0.0,
),
crawl= dict(
height= (0.25, 0.5),
@ -98,14 +97,13 @@ class A1FieldCfg( A1RoughCfg ):
virtual_terrain= False,
draw_virtual_terrain= True,
engaging_next_threshold= 1.2,
engaging_finish_threshold= 0.,
curriculum_perlin= False,
no_perlin_threshold= 0.1,
no_perlin_threshold= 0.0,
)
TerrainPerlin_kwargs = dict(
zScale= [0.08, 0.15],
# zScale= 0.15, # Use a constant zScale for training a walk policy
zScale= [0.1, 0.15],
# zScale= 0.1, # Use a constant zScale for training a walk policy
frequency= 10,
)
@ -116,6 +114,9 @@ class A1FieldCfg( A1RoughCfg ):
lin_vel_x = [-1.0, 1.0]
lin_vel_y = [0.0, 0.0]
ang_vel_yaw = [0., 0.]
######## configs for training a walk policy #########
# lin_vel_y = [-1., 1.]
# ang_vel_yaw = [-1., 1.]
class control( A1RoughCfg.control ):
stiffness = {'joint': 50.}
@ -124,15 +125,10 @@ class A1FieldCfg( A1RoughCfg ):
torque_limits = 25 # override the urdf
computer_clip_torque = True
motor_clip_torque = False
# action_delay = False # set to True to enable action delay in sim
# action_delay_range = [0.002, 0.022] # [s]
# action_delay_resample_time = 5.0 # [s]
class asset( A1RoughCfg.asset ):
penalize_contacts_on = ["base", "thigh", "calf"]
penalize_contacts_on = ["base", "thigh"]
terminate_after_contacts_on = ["base", "imu"]
front_hip_names = ["FR_hip_joint", "FL_hip_joint"]
rear_hip_names = ["RR_hip_joint", "RL_hip_joint"]
class termination:
# additional factors that determines whether to terminates the episode
@ -149,8 +145,8 @@ class A1FieldCfg( A1RoughCfg ):
tilt_threshold= 1.5,
)
pitch_kwargs = dict(
threshold= 1.6, # [rad] # for leap, jump
jump_threshold= 1.6,
threshold= 1.6, # [rad] # for leap, climb
climb_threshold= 1.6,
leap_threshold= 1.5,
)
z_low_kwargs = dict(
@ -180,7 +176,7 @@ class A1FieldCfg( A1RoughCfg ):
added_mass_range = [1.0, 3.0]
randomize_friction = True
friction_range = [0., 2.]
friction_range = [0.0, 1.]
init_base_pos_range = dict(
x= [0.2, 0.6],
@ -199,34 +195,15 @@ class A1FieldCfg( A1RoughCfg ):
# penalty for hardware safety
exceed_dof_pos_limits = -1e-1
exceed_torque_limits_i = -2e-1
soft_dof_pos_limit = 0.01
soft_dof_pos_limit = 0.9
class normalization( A1RoughCfg.normalization ):
class obs_scales( A1RoughCfg.normalization.obs_scales ):
forward_depth = 1.
base_pose = [0., 0., 0., 1., 1., 1.]
base_pose = [0., 0., 1., 1., 1., 1.]
engaging_block = 1.
""" The following action clip is used for tanh policy activation. """
# clip_actions_method = "hard"
# dof_pos_redundancy = 0.2
# clip_actions_low = []
# clip_actions_high = []
# for sdk_joint_name, sim_joint_name in zip(
# ["Hip", "Thigh", "Calf"] * 4,
# [ # in the order as simulation
# "FL_hip_joint", "FL_thigh_joint", "FL_calf_joint",
# "FR_hip_joint", "FR_thigh_joint", "FR_calf_joint",
# "RL_hip_joint", "RL_thigh_joint", "RL_calf_joint",
# "RR_hip_joint", "RR_thigh_joint", "RR_calf_joint",
# ],
# ): # This setting is only for position control.
# clip_actions_low.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_min"] + dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
# clip_actions_high.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_max"] - dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
# del dof_pos_redundancy, sdk_joint_name, sim_joint_name # This is not intended to be an attribute of normalization
""" The above action clip is used for tanh policy activation. """
class noise( A1RoughCfg.noise ):
add_noise = False # disable internal uniform +- 1 noise, and no noise in proprioception
class noise_scales( A1RoughCfg.noise.noise_scales ):
forward_depth = 0.1
base_pose = 1.0
@ -267,7 +244,6 @@ class A1FieldCfg( A1RoughCfg ):
no_moveup_when_fall = False
# chosen heuristically, please refer to `LeggedRobotField._get_terrain_curriculum_move` with fixed body_measure_points
logs_root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))), "logs")
class A1FieldCfgPPO( A1RoughCfgPPO ):
class algorithm( A1RoughCfgPPO.algorithm ):
entropy_coef = 0.01
@ -280,9 +256,8 @@ class A1FieldCfgPPO( A1RoughCfgPPO ):
class runner( A1RoughCfgPPO.runner ):
policy_class_name = "ActorCriticRecurrent"
experiment_name = "field_a1"
resume = False
run_name = "".join(["WalkForward",
run_name = "".join(["WalkingBase",
("_pEnergySubsteps" + np.format_float_scientific(-A1FieldCfg.rewards.scales.legs_energy_substeps, precision=1, exp_digits=1, trim="-") if A1FieldCfg.rewards.scales.legs_energy_substeps != 0 else ""),
("_propDelay{:.2f}-{:.2f}".format(
A1FieldCfg.sensor.proprioception.latency_range[0],
A1FieldCfg.sensor.proprioception.latency_range[1],

302
legged_gym/legged_gym/envs/a1/a1_field_config_new.py Normal file
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@ -0,0 +1,302 @@
import numpy as np
import os.path as osp
from legged_gym.envs.a1.a1_config import A1RoughCfg, A1RoughCfgPPO
class A1FieldCfg( A1RoughCfg ):
class env( A1RoughCfg.env ):
num_envs = 4096 # 8192
obs_components = [
"proprioception", # 48
# "height_measurements", # 187
"base_pose",
"robot_config",
"engaging_block",
"sidewall_distance",
]
# privileged_use_lin_vel = True # for the possible of setting "proprioception" in obs and privileged obs different
######## configs for training a walk policy ############
# obs_components = [
# "proprioception", # 48
# # "height_measurements", # 187
# # "forward_depth",
# # "base_pose",
# # "robot_config",
# # "engaging_block",
# # "sidewall_distance",
# ]
# privileged_obs_components = [
# "proprioception",
# # "height_measurements",
# # "forward_depth",
# "robot_config",
# ]
######## End configs for training a walk policy ############
class sensor:
class forward_camera:
resolution = [16, 16]
position = [0.26, 0., 0.03] # position in base_link
rotation = [0., 0., 0.] # ZYX Euler angle in base_link
class proprioception:
delay_action_obs = False
latency_range = [0.0, 0.0]
latency_resample_time = 2.0 # [s]
class terrain( A1RoughCfg.terrain ):
mesh_type = "trimesh" # Don't change
num_rows = 20
num_cols = 50
selected = "BarrierTrack" # "BarrierTrack" or "TerrainPerlin", "TerrainPerlin" can be used for training a walk policy.
max_init_terrain_level = 0
border_size = 5
slope_treshold = 20.
curriculum = False # for walk
horizontal_scale = 0.025 # [m]
# vertical_scale = 1. # [m] does not change the value in hightfield
pad_unavailable_info = True
BarrierTrack_kwargs = dict(
options= [
# "jump",
# "crawl",
# "tilt",
# "leap",
], # each race track will permute all the options
track_width= 1.6,
track_block_length= 2., # the x-axis distance from the env origin point
wall_thickness= (0.04, 0.2), # [m]
wall_height= -0.05,
jump= dict(
height= (0.2, 0.6),
depth= (0.1, 0.8), # size along the forward axis
fake_offset= 0.0, # [m] an offset that make the robot easier to get into the obstacle
jump_down_prob= 0., # probability of jumping down use it in non-virtual terrain
),
crawl= dict(
height= (0.25, 0.5),
depth= (0.1, 0.6), # size along the forward axis
wall_height= 0.6,
no_perlin_at_obstacle= False,
),
tilt= dict(
width= (0.24, 0.32),
depth= (0.4, 1.), # size along the forward axis
opening_angle= 0.0, # [rad] an opening that make the robot easier to get into the obstacle
wall_height= 0.5,
),
leap= dict(
length= (0.2, 1.0),
depth= (0.4, 0.8),
height= 0.2,
),
add_perlin_noise= True,
border_perlin_noise= True,
border_height= 0.,
virtual_terrain= False,
draw_virtual_terrain= True,
engaging_next_threshold= 1.2,
engaging_finish_threshold= 0.,
curriculum_perlin= False,
no_perlin_threshold= 0.1,
)
TerrainPerlin_kwargs = dict(
zScale= [0.08, 0.15],
# zScale= 0.15, # Use a constant zScale for training a walk policy
frequency= 10,
)
class commands( A1RoughCfg.commands ):
heading_command = False
resampling_time = 10 # [s]
class ranges( A1RoughCfg.commands.ranges ):
lin_vel_x = [-1.0, 1.0]
lin_vel_y = [0.0, 0.0]
ang_vel_yaw = [0., 0.]
class control( A1RoughCfg.control ):
stiffness = {'joint': 50.}
damping = {'joint': 1.}
action_scale = 0.5
torque_limits = 25 # override the urdf
computer_clip_torque = True
motor_clip_torque = False
# action_delay = False # set to True to enable action delay in sim
# action_delay_range = [0.002, 0.022] # [s]
# action_delay_resample_time = 5.0 # [s]
class asset( A1RoughCfg.asset ):
penalize_contacts_on = ["base", "thigh", "calf"]
terminate_after_contacts_on = ["base", "imu"]
front_hip_names = ["FR_hip_joint", "FL_hip_joint"]
rear_hip_names = ["RR_hip_joint", "RL_hip_joint"]
class termination:
# additional factors that determines whether to terminates the episode
termination_terms = [
"roll",
"pitch",
"z_low",
"z_high",
# "out_of_track",
]
roll_kwargs = dict(
threshold= 0.8, # [rad]
tilt_threshold= 1.5,
)
pitch_kwargs = dict(
threshold= 1.6, # [rad] # for leap, jump
jump_threshold= 1.6,
leap_threshold= 1.5,
)
z_low_kwargs = dict(
threshold= 0.15, # [m]
)
z_high_kwargs = dict(
threshold= 1.5, # [m]
)
out_of_track_kwargs = dict(
threshold= 1., # [m]
)
check_obstacle_conditioned_threshold = True
timeout_at_border = False
class domain_rand( A1RoughCfg.domain_rand ):
randomize_com = True
class com_range:
x = [-0.05, 0.15]
y = [-0.1, 0.1]
z = [-0.05, 0.05]
randomize_motor = True
leg_motor_strength_range = [0.9, 1.1]
randomize_base_mass = True
added_mass_range = [1.0, 3.0]
randomize_friction = True
friction_range = [0., 2.]
init_base_pos_range = dict(
x= [0.2, 0.6],
y= [-0.25, 0.25],
)
push_robots = False
class rewards( A1RoughCfg.rewards ):
class scales:
tracking_ang_vel = 0.05
world_vel_l2norm = -1.
legs_energy_substeps = -2e-5
legs_energy = -0.
alive = 2.
# penalty for hardware safety
exceed_dof_pos_limits = -1e-1
exceed_torque_limits_i = -2e-1
soft_dof_pos_limit = 0.01
class normalization( A1RoughCfg.normalization ):
class obs_scales( A1RoughCfg.normalization.obs_scales ):
forward_depth = 1.
base_pose = [0., 0., 0., 1., 1., 1.]
engaging_block = 1.
""" The following action clip is used for tanh policy activation. """
# clip_actions_method = "hard"
# dof_pos_redundancy = 0.2
# clip_actions_low = []
# clip_actions_high = []
# for sdk_joint_name, sim_joint_name in zip(
# ["Hip", "Thigh", "Calf"] * 4,
# [ # in the order as simulation
# "FL_hip_joint", "FL_thigh_joint", "FL_calf_joint",
# "FR_hip_joint", "FR_thigh_joint", "FR_calf_joint",
# "RL_hip_joint", "RL_thigh_joint", "RL_calf_joint",
# "RR_hip_joint", "RR_thigh_joint", "RR_calf_joint",
# ],
# ): # This setting is only for position control.
# clip_actions_low.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_min"] + dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
# clip_actions_high.append( (A1RoughCfg.asset.sdk_dof_range[sdk_joint_name + "_max"] - dof_pos_redundancy - A1RoughCfg.init_state.default_joint_angles[sim_joint_name]) / a1_action_scale )
# del dof_pos_redundancy, sdk_joint_name, sim_joint_name # This is not intended to be an attribute of normalization
""" The above action clip is used for tanh policy activation. """
class noise( A1RoughCfg.noise ):
add_noise = False # disable internal uniform +- 1 noise, and no noise in proprioception
class noise_scales( A1RoughCfg.noise.noise_scales ):
forward_depth = 0.1
base_pose = 1.0
class viewer( A1RoughCfg.viewer ):
pos = [0, 0, 5] # [m]
lookat = [5., 5., 2.] # [m]
draw_volume_sample_points = False
class sim( A1RoughCfg.sim ):
body_measure_points = { # transform are related to body frame
"base": dict(
x= [i for i in np.arange(-0.2, 0.31, 0.03)],
y= [-0.08, -0.04, 0.0, 0.04, 0.08],
z= [i for i in np.arange(-0.061, 0.061, 0.03)],
transform= [0., 0., 0.005, 0., 0., 0.],
),
"thigh": dict(
x= [
-0.16, -0.158, -0.156, -0.154, -0.152,
-0.15, -0.145, -0.14, -0.135, -0.13, -0.125, -0.12, -0.115, -0.11, -0.105, -0.1, -0.095, -0.09, -0.085, -0.08, -0.075, -0.07, -0.065, -0.05,
0.0, 0.05, 0.1,
],
y= [-0.015, -0.01, 0.0, -0.01, 0.015],
z= [-0.03, -0.015, 0.0, 0.015],
transform= [0., 0., -0.1, 0., 1.57079632679, 0.],
),
"calf": dict(
x= [i for i in np.arange(-0.13, 0.111, 0.03)],
y= [-0.015, 0.0, 0.015],
z= [-0.015, 0.0, 0.015],
transform= [0., 0., -0.11, 0., 1.57079632679, 0.],
),
}
class curriculum:
no_moveup_when_fall = False
# chosen heuristically, please refer to `LeggedRobotField._get_terrain_curriculum_move` with fixed body_measure_points
logs_root = osp.join(osp.dirname(osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))), "logs")
class A1FieldCfgPPO( A1RoughCfgPPO ):
class algorithm( A1RoughCfgPPO.algorithm ):
entropy_coef = 0.01
clip_min_std = 1e-12
class policy( A1RoughCfgPPO.policy ):
rnn_type = 'gru'
mu_activation = "tanh"
class runner( A1RoughCfgPPO.runner ):
policy_class_name = "ActorCriticRecurrent"
experiment_name = "field_a1"
resume = False
run_name = "".join(["WalkForward",
("_propDelay{:.2f}-{:.2f}".format(
A1FieldCfg.sensor.proprioception.latency_range[0],
A1FieldCfg.sensor.proprioception.latency_range[1],
) if A1FieldCfg.sensor.proprioception.delay_action_obs else ""
),
("_aScale{:d}{:d}{:d}".format(
int(A1FieldCfg.control.action_scale[0] * 10),
int(A1FieldCfg.control.action_scale[1] * 10),
int(A1FieldCfg.control.action_scale[2] * 10),
) if isinstance(A1FieldCfg.control.action_scale, (tuple, list)) \
else "_aScale{:.1f}".format(A1FieldCfg.control.action_scale)
),
])
resume = False
max_iterations = 10000
save_interval = 500

2
legged_gym/legged_gym/envs/a1/a1_field_distill_config.py
View File

@ -3,7 +3,7 @@ import os
import os.path as osp
from datetime import datetime
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1FieldDistillCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/a1/a1_jump_config.py
View File

@ -1,6 +1,6 @@
from os import path as osp
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1JumpCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/a1/a1_leap_config.py
View File

@ -1,5 +1,5 @@
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1LeapCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/a1/a1_remote_config.py
View File

@ -1,6 +1,6 @@
import numpy as np
import os.path as osp
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1RemoteCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/a1/a1_tilt_config.py
View File

@ -1,5 +1,5 @@
import numpy as np
from legged_gym.envs.a1.a1_field_config import A1FieldCfg, A1FieldCfgPPO
from legged_gym.envs.a1.a1_field_config_new import A1FieldCfg, A1FieldCfgPPO
from legged_gym.utils.helpers import merge_dict
class A1TiltCfg( A1FieldCfg ):

2
legged_gym/legged_gym/envs/base/legged_robot.py
View File

@ -578,7 +578,7 @@ class LeggedRobot(BaseTask):
self.all_root_states = gymtorch.wrap_tensor(actor_root_state)
self.root_states = self.all_root_states.view(self.num_envs, -1, 13)[:, 0, :] # (num_envs, 13)
self.all_dof_states = gymtorch.wrap_tensor(dof_state_tensor)
self.dof_state = self.all_dof_states.view(self.num_envs, -1, 2)[:, :self.num_dof, :] # (num_envs, 2)
self.dof_state = self.all_dof_states.view(self.num_envs, -1, 2)[:, :self.num_dof, :] # (num_envs, 2) position + velocity
self.dof_pos = self.dof_state.view(self.num_envs, -1, 2)[..., :self.num_dof, 0]
self.dof_vel = self.dof_state.view(self.num_envs, -1, 2)[..., :self.num_dof, 1]
self.base_quat = self.root_states[:, 3:7]

4
legged_gym/legged_gym/scripts/play.py
View File

@ -107,8 +107,8 @@ def play(args):
]
if "one_obstacle_per_track" in env_cfg.terrain.BarrierTrack_kwargs.keys():
env_cfg.terrain.BarrierTrack_kwargs.pop("one_obstacle_per_track")
env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 2
env_cfg.commands.ranges.lin_vel_x = [1.2, 1.2]
env_cfg.terrain.BarrierTrack_kwargs["n_obstacles_per_track"] = 0# 2
env_cfg.commands.ranges.lin_vel_x = [1.2, 1.2] # [1.2, 1.2]
if "distill" in args.task:
env_cfg.commands.ranges.lin_vel_x = [0.0, 0.0]
env_cfg.commands.ranges.lin_vel_y = [-0., 0.]

2
legged_gym/legged_gym/utils/terrain/barrier_track.py
View File

@ -750,7 +750,7 @@ class BarrierTrack:
block_idx,
0.,
(self.n_blocks_per_track - 1),
)
).to(int)
# compute whether the robot is still in any of the track
in_track_mask = (track_idx == track_idx_clipped).all(dim= -1) & (block_idx == block_idx_clipped)