Apollo3D/Gym_CPU
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

restore

Browse Source
This commit is contained in:
xxh
2026-03-13 21:44:59 -04:00
parent cf80becd17
commit 294fe0bd79
1 changed files with 639 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

639
scripts/gyms/Walk.py Normal file
View File

@@ -0,0 +1,639 @@
import os
import numpy as np
import math
import time
from time import sleep
from random import random
from random import uniform
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
import gymnasium as gym
from gymnasium import spaces
from scripts.commons.Train_Base import Train_Base
from scripts.commons.Server import Server as Train_Server
from agent.base_agent import Base_Agent
from utils.math_ops import MathOps
from scipy.spatial.transform import Rotation as R
'''
Objective:
Learn how to run forward using step primitive
----------
- class Basic_Run: implements an OpenAI custom gym
- class Train: implements algorithms to train a new model or test an existing model
'''
class WalkEnv(gym.Env):
def __init__(self, ip, server_p) -> None:
# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
self.Player = player = Base_Agent(
team_name="Gym",
number=1,
host=ip,
port=server_p
)
self.robot_type = self.Player.robot
self.step_counter = 0 # to limit episode size
self.force_play_on = True
self.target_position = np.array([0.0, 0.0]) # target position in the x-y plane
self.initial_position = np.array([0.0, 0.0]) # initial position in the x-y plane
self.target_direction = 0.0 # target direction in the x-y plane (relative to the robot's orientation)
self.isfallen = False
self.waypoint_index = 0
self.route_completed = False
self.debug_every_n_steps = 5
self.calibrate_nominal_from_neutral = True
self.auto_calibrate_train_sim_flip = True
self.nominal_calibrated_once = False
self.flip_calibrated_once = False
self._target_hz = 0.0
self._target_dt = 0.0
self._last_sync_time = None
target_hz_env = 1000
if target_hz_env:
try:
self._target_hz = float(target_hz_env)
except ValueError:
self._target_hz = 0.0
if self._target_hz > 0.0:
self._target_dt = 1.0 / self._target_hz
# State space
# 原始观测大小: 78
obs_size = 78
self.obs = np.zeros(obs_size, np.float32)
self.observation_space = spaces.Box(
low=-10.0,
high=10.0,
shape=(obs_size,),
dtype=np.float32
)
action_dim = len(self.Player.robot.ROBOT_MOTORS)
self.no_of_actions = action_dim
self.action_space = spaces.Box(
low=-1.0,
high=1.0,
shape=(action_dim,),
dtype=np.float32
)
# 中立姿态
self.joint_nominal_position = np.array(
[
0.0,
0.0,
0.0,
1.4,
0.0,
-0.4,
0.0,
-1.4,
0.0,
0.4,
0.0,
-0.4,
0.0,
0.0,
0.8,
-0.4,
0.0,
0.4,
0.0,
0.0,
-0.8,
0.4,
0.0,
]
)
self.reference_joint_nominal_position = self.joint_nominal_position.copy()
self.train_sim_flip = np.array(
[
1.0, # 0: Head_yaw (he1)
-1.0, # 1: Head_pitch (he2)
1.0, # 2: Left_Shoulder_Pitch (lae1)
-1.0, # 3: Left_Shoulder_Roll (lae2)
1.0, # 4: Left_Elbow_Pitch (lae3)
1.0, # 5: Left_Elbow_Yaw (lae4)
-1.0, # 6: Right_Shoulder_Pitch (rae1)
-1.0, # 7: Right_Shoulder_Roll (rae2)
1.0, # 8: Right_Elbow_Pitch (rae3)
1.0, # 9: Right_Elbow_Yaw (rae4)
1.0, # 10: Waist (te1)
1.0, # 11: Left_Hip_Pitch (lle1)
-1.0, # 12: Left_Hip_Roll (lle2)
-1.0, # 13: Left_Hip_Yaw (lle3)
1.0, # 14: Left_Knee_Pitch (lle4)
1.0, # 15: Left_Ankle_Pitch (lle5)
-1.0, # 16: Left_Ankle_Roll (lle6)
-1.0, # 17: Right_Hip_Pitch (rle1)
-1.0, # 18: Right_Hip_Roll (rle2)
-1.0, # 19: Right_Hip_Yaw (rle3)
-1.0, # 20: Right_Knee_Pitch (rle4)
-1.0, # 21: Right_Ankle_Pitch (rle5)
-1.0, # 22: Right_Ankle_Roll (rle6)
]
)
self.scaling_factor = 0.5
self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
self.previous_pos = np.array([0.0, 0.0]) # Track previous position
self.Player.server.connect()
# sleep(2.0) # Longer wait for connection to establish completely
self.Player.server.send_immediate(
f"(init {self.Player.robot.name} {self.Player.world.team_name} {self.Player.world.number})"
)
def debug_log(self, message):
print(message)
try:
log_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "comm_debug.log")
with open(log_path, "a", encoding="utf-8") as f:
f.write(message + "\n")
except OSError:
pass
def calibrate_train_sim_flip_from_neutral(self, neutral_joint_positions):
updated_flip = self.train_sim_flip.copy()
changed = []
for idx, (reference_value, observed_value) in enumerate(
zip(self.reference_joint_nominal_position, neutral_joint_positions)
):
if idx >= 10:
continue
if abs(reference_value) < 0.15 or abs(observed_value) < 0.15:
continue
inferred_flip = 1.0 if np.sign(reference_value) == np.sign(observed_value) else -1.0
if updated_flip[idx] != inferred_flip:
changed.append((idx, updated_flip[idx], inferred_flip))
updated_flip[idx] = inferred_flip
self.train_sim_flip = updated_flip
if changed:
self.debug_log(
"[FlipDebug] "
f"changes={[(idx, old, new) for idx, old, new in changed]}"
)
def is_reliable_neutral_pose(self, neutral_joint_positions):
leg_positions = neutral_joint_positions[11:]
leg_norm = float(np.linalg.norm(leg_positions))
leg_max = float(np.max(np.abs(leg_positions)))
height = float(self.Player.world.global_position[2])
reliable = (
leg_norm > 0.8
and leg_max > 0.35
and 0.12 < height < 0.8
)
return reliable, leg_norm, leg_max, height
def observe(self, init=False):
"""获取当前观测值"""
robot = self.Player.robot
world = self.Player.world
# Safety check: ensure data is available
# 计算目标速度
raw_target = self.target_position - world.global_position[:2]
velocity = MathOps.rotate_2d_vec(
raw_target,
-robot.global_orientation_euler[2],
is_rad=False
)
# 计算相对方向
rel_orientation = MathOps.vector_angle(velocity) * 0.3
rel_orientation = np.clip(rel_orientation, -0.25, 0.25)
velocity = np.concatenate([velocity, np.array([rel_orientation])])
velocity[0] = np.clip(velocity[0], -0.5, 0.5)
velocity[1] = np.clip(velocity[1], -0.25, 0.25)
# 关节状态
radian_joint_positions = np.deg2rad(
[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
)
radian_joint_speeds = np.deg2rad(
[robot.motor_speeds[motor] for motor in robot.ROBOT_MOTORS]
)
qpos_qvel_previous_action = np.concatenate([
(radian_joint_positions * self.train_sim_flip - self.joint_nominal_position) / 4.6,
radian_joint_speeds / 110.0 * self.train_sim_flip,
self.previous_action / 10.0,
])
# 角速度
ang_vel = np.clip(np.deg2rad(robot.gyroscope) / 50.0, -1.0, 1.0)
# 投影的重力方向
orientation_quat_inv = R.from_quat(robot._global_cheat_orientation).inv()
projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
# 组合观测
observation = np.concatenate([
qpos_qvel_previous_action,
ang_vel,
velocity,
projected_gravity,
])
observation = np.clip(observation, -10.0, 10.0)
return observation.astype(np.float32)
def sync(self):
''' Run a single simulation step '''
self.Player.server.receive()
self.Player.world.update()
self.Player.robot.commit_motor_targets_pd()
self.Player.server.send()
if self._target_dt > 0.0:
now = time.time()
if self._last_sync_time is None:
self._last_sync_time = now
return
elapsed = now - self._last_sync_time
remaining = self._target_dt - elapsed
if remaining > 0.0:
time.sleep(remaining)
now = time.time()
self._last_sync_time = now
def debug_joint_status(self):
robot = self.Player.robot
actual_joint_positions = np.deg2rad(
[robot.motor_positions[motor] for motor in robot.ROBOT_MOTORS]
)
target_joint_positions = getattr(
self,
'target_joint_positions',
np.zeros(len(robot.ROBOT_MOTORS), dtype=np.float32)
)
joint_error = actual_joint_positions - target_joint_positions
leg_slice = slice(11, None)
self.debug_log(
"[WalkDebug] "
f"step={self.step_counter} "
f"pos={np.round(self.Player.world.global_position, 3).tolist()} "
f"target_xy={np.round(self.target_position, 3).tolist()} "
f"target_leg={np.round(target_joint_positions[leg_slice], 3).tolist()} "
f"actual_leg={np.round(actual_joint_positions[leg_slice], 3).tolist()} "
f"err_norm={float(np.linalg.norm(joint_error)):.4f} "
f"fallen={self.Player.world.global_position[2] < 0.3}"
)
def reset(self, seed=None, options=None):
'''
Reset and stabilize the robot
Note: for some behaviors it would be better to reduce stabilization or add noise
'''
r = self.Player.robot
super().reset(seed=seed)
if seed is not None:
np.random.seed(seed)
length1 = np.random.uniform(10, 20) # randomize target distance
length2 = np.random.uniform(10, 20) # randomize target distance
length3 = np.random.uniform(10, 20) # randomize target distance
angle2 = np.random.uniform(-30, 30) # randomize initial orientation
angle3 = np.random.uniform(-30, 30) # randomize target direction
self.step_counter = 0
self.waypoint_index = 0
self.route_completed = False
self.previous_action = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
self.last_action_for_reward = np.zeros(len(self.Player.robot.ROBOT_MOTORS))
self.previous_pos = np.array([0.0, 0.0]) # Initialize for first step
self.walk_cycle_step = 0
# 随机 beam 目标位置和朝向，增加训练多样性
beam_x = (random() - 0.5) * 10
beam_y = (random() - 0.5) * 10
for _ in range(5):
self.Player.server.receive()
self.Player.world.update()
self.Player.robot.commit_motor_targets_pd()
self.Player.server.commit_beam(pos2d=(beam_x, beam_y), rotation=0)
self.Player.server.send()
# 执行 Neutral 技能直到完成，给机器人足够时间在 beam 位置稳定站立
finished_count = 0
for _ in range(10):
finished = self.Player.skills_manager.execute("Neutral")
self.sync()
if finished:
finished_count += 1
if finished_count >= 3: # 假设需要连续3次完成才算成功
break
# neutral_joint_positions = np.deg2rad(
# [self.Player.robot.motor_positions[motor] for motor in self.Player.robot.ROBOT_MOTORS]
# )
# reliable_neutral, neutral_leg_norm, neutral_leg_max, neutral_height = self.is_reliable_neutral_pose(neutral_joint_positions)
# if self.auto_calibrate_train_sim_flip and reliable_neutral and not self.flip_calibrated_once:
# self.calibrate_train_sim_flip_from_neutral(neutral_joint_positions)
# self.flip_calibrated_once = True
# if self.calibrate_nominal_from_neutral and reliable_neutral and not self.nominal_calibrated_once:
# self.joint_nominal_position = neutral_joint_positions * self.train_sim_flip
# self.nominal_calibrated_once = True
# self.debug_log(
# "[ResetDebug] "
# f"neutral_pos={np.round(self.Player.world.global_position, 3).tolist()} "
# f"shoulders={np.round(neutral_joint_positions[2:10], 3).tolist()} "
# f"legs={np.round(neutral_joint_positions[11:], 3).tolist()} "
# f"flip={self.train_sim_flip.tolist()} "
# f"nominal_legs={np.round(self.joint_nominal_position[11:], 3).tolist()} "
# f"calibrated_once={(self.flip_calibrated_once, self.nominal_calibrated_once)} "
# f"reliable_neutral={reliable_neutral} "
# f"leg_norm={neutral_leg_norm:.3f} leg_max={neutral_leg_max:.3f} height={neutral_height:.3f}"
# )
# reset_action_noise = np.random.uniform(-0.015, 0.015, size=(len(self.Player.robot.ROBOT_MOTORS),))
# self.target_joint_positions = (self.joint_nominal_position + reset_action_noise) * self.train_sim_flip
# for idx, target in enumerate(self.target_joint_positions):
# r.set_motor_target_position(
# r.ROBOT_MOTORS[idx], target*180/math.pi, kp=25, kd=0.6
# )
# memory variables
self.initial_position = np.array(self.Player.world.global_position[:2])
self.previous_pos = self.initial_position.copy() # Critical: set to actual position
self.act = np.zeros(self.no_of_actions, np.float32)
point1 = self.initial_position + np.array([length1, 0])
point2 = point1 + MathOps.rotate_2d_vec(np.array([length2, 0]), angle2, is_rad=False)
point3 = point2 + MathOps.rotate_2d_vec(np.array([length3, 0]), angle3, is_rad=False)
self.point_list = [point1, point2, point3]
self.target_position = self.point_list[self.waypoint_index]
return self.observe(True), {}
def render(self, mode='human', close=False):
return
def compute_reward(self, previous_pos, current_pos, action):
eps = 1e-6
dt = 0.05
velocity = current_pos - previous_pos
speed_step = float(np.linalg.norm(velocity))
speed = speed_step / dt
direction_to_target = self.target_position - current_pos
prev_direction_to_target = self.target_position - previous_pos
distance_to_target = float(np.linalg.norm(direction_to_target))
prev_distance_to_target = float(np.linalg.norm(prev_direction_to_target))
# Progress toward waypoint (secondary signal)
progress = prev_distance_to_target - distance_to_target
progress_reward = np.clip(progress * 2.0, -1.5, 2.5)
# Forward speed and lateral drift
forward_dir = direction_to_target / max(distance_to_target, eps)
forward_speed = float(np.dot(velocity, forward_dir)) / dt
target_speed = 1.0
speed_error = forward_speed - target_speed
speed_reward = 3.0 * math.exp(-1.5 * (speed_error ** 2))
lateral_vec = velocity - forward_dir * np.dot(velocity, forward_dir)
lateral_speed = float(np.linalg.norm(lateral_vec)) / dt
lateral_penalty = -0.6 * np.clip(lateral_speed, 0.0, 2.0)
# Heading alignment (small shaping term)
if speed_step > 1e-4 and distance_to_target > 1e-4:
directional_alignment = np.dot(velocity, direction_to_target) / (speed_step * distance_to_target)
directional_alignment = float(np.clip(directional_alignment, -1.0, 1.0))
direction_reward = max(0.0, directional_alignment) * 0.3
else:
direction_reward = 0.0
alive_bonus = 0.05
# Height and posture
height = float(self.Player.world.global_position[2])
if 0.8 <= height <= 1.05:
height_reward = 1.0
elif 0.40 <= height <= 1.20:
height_reward = -1.0
else:
height_reward = -6.0
orientation_quat_inv = R.from_quat(self.Player.robot._global_cheat_orientation).inv()
projected_gravity = orientation_quat_inv.apply(np.array([0.0, 0.0, -1.0]))
tilt_mag = float(np.linalg.norm(projected_gravity[:2]))
posture_penalty = -2.2 * (tilt_mag ** 2)
motionless_penalty = -1.5 if speed < 0.1 else 0.0
# Waypoint bonus
waypoint_bonus = 0.0
if distance_to_target < 0.5:
waypoint_bonus = 25.0
if self.waypoint_index < len(self.point_list) - 1:
self.waypoint_index += 1
self.target_position = self.point_list[self.waypoint_index]
else:
waypoint_bonus = 100.0
self.route_completed = True
# Effort + smoothness
action_magnitude = float(np.linalg.norm(action[11:]))
action_penalty = -0.05 * action_magnitude
action_delta = action - self.last_action_for_reward
smoothness_penalty = -0.02 * float(np.linalg.norm(action_delta[11:]))
return (
progress_reward
+ speed_reward
+ lateral_penalty
+ direction_reward
+ alive_bonus
+ height_reward
+ posture_penalty
+ motionless_penalty
+ waypoint_bonus
+ action_penalty
+ smoothness_penalty
)
def step(self, action):
r = self.Player.robot
self.previous_action = action
self.target_joint_positions = (
self.joint_nominal_position
+ self.scaling_factor * action
)
self.target_joint_positions *= self.train_sim_flip
for idx, target in enumerate(self.target_joint_positions):
r.set_motor_target_position(
r.ROBOT_MOTORS[idx], target * 180 / math.pi, kp=25, kd=0.6
)
self.sync() # run simulation step
self.step_counter += 1
# if self.step_counter % self.debug_every_n_steps == 0:
# self.debug_joint_status()
current_pos = np.array(self.Player.world.global_position[:2], dtype=np.float32)
# Compute reward based on movement from previous step
reward = self.compute_reward(self.previous_pos, current_pos, action)
# Update previous position
self.previous_pos = current_pos.copy()
self.last_action_for_reward = action.copy()
# Fall detection and penalty
is_fallen = self.Player.world.global_position[2] < 0.3
# terminal state: the robot is falling or timeout
terminated = is_fallen or self.step_counter > 800 or self.route_completed
truncated = False
return self.observe(), reward, terminated, truncated, {}
class Train(Train_Base):
def __init__(self, script) -> None:
super().__init__(script)
def train(self, args):
# --------------------------------------- Learning parameters
n_envs = 20 # Reduced from 8 to decrease CPU/network pressure during init
if n_envs < 1:
raise ValueError("GYM_CPU_N_ENVS must be >= 1")
n_steps_per_env = 512 # RolloutBuffer is of size (n_steps_per_env * n_envs)
minibatch_size = 128 # should be a factor of (n_steps_per_env * n_envs)
total_steps = 30000000
learning_rate = 3e-4
folder_name = f'Walk_R{self.robot_type}'
model_path = f'./scripts/gyms/logs/{folder_name}/'
print(f"Model path: {model_path}")
print(f"Using {n_envs} parallel environments")
# --------------------------------------- Run algorithm
def init_env(i_env):
def thunk():
return WalkEnv(self.ip, self.server_p + i_env)
return thunk
server_log_dir = os.path.join(model_path, "server_logs")
os.makedirs(server_log_dir, exist_ok=True)
servers = Train_Server(self.server_p, self.monitor_p_1000, n_envs + 1) # include 1 extra server for testing
# Wait for servers to start
print(f"Starting {n_envs + 1} rcssservermj servers...")
print("Servers started, creating environments...")
env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
eval_env = SubprocVecEnv([init_env(n_envs)])
try:
# Custom policy network architecture
policy_kwargs = dict(
net_arch=dict(
pi=[256, 256, 128], # Policy network: 3 layers
vf=[256, 256, 128] # Value network: 3 layers
),
activation_fn=__import__('torch.nn', fromlist=['ReLU']).ReLU,
)
if "model_file" in args: # retrain
model = PPO.load(args["model_file"], env=env, device="cpu", n_envs=n_envs, n_steps=n_steps_per_env,
batch_size=minibatch_size, learning_rate=learning_rate)
else: # train new model
model = PPO(
"MlpPolicy",
env=env,
verbose=1,
n_steps=n_steps_per_env,
batch_size=minibatch_size,
learning_rate=learning_rate,
device="cpu",
policy_kwargs=policy_kwargs,
ent_coef=0.01, # Entropy coefficient for exploration
clip_range=0.2, # PPO clipping parameter
gae_lambda=0.95, # GAE lambda
gamma=0.99 # Discount factor
)
model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
eval_freq=n_steps_per_env * 10, save_freq=n_steps_per_env * 10,
backup_env_file=__file__)
except KeyboardInterrupt:
sleep(1) # wait for child processes
print("\nctrl+c pressed, aborting...\n")
servers.kill()
return
env.close()
eval_env.close()
servers.kill()
def test(self, args):
# Uses different server and monitor ports
server_log_dir = os.path.join(args["folder_dir"], "server_logs")
os.makedirs(server_log_dir, exist_ok=True)
server = Train_Server(self.server_p - 1, self.monitor_p, 1)
env = WalkEnv(self.ip, self.server_p - 1)
model = PPO.load(args["model_file"], env=env)
try:
self.export_model(args["model_file"], args["model_file"] + ".pkl",
False) # Export to pkl to create custom behavior
self.test_model(model, env, log_path=args["folder_dir"], model_path=args["folder_dir"])
except KeyboardInterrupt:
print()
env.close()
server.kill()
if __name__ == "__main__":
from types import SimpleNamespace
# 创建默认参数
script_args = SimpleNamespace(
args=SimpleNamespace(
i='127.0.0.1', # Server IP
p=3100, # Server port
m=3200, # Monitor port
r=0, # Robot type
t='Gym', # Team name
u=1 # Uniform number
)
)
trainer = Train(script_args)
trainer.train({})
# trainer.test({"model_file": "scripts/gyms/logs/Walk_R0_003/best_model.zip",
# "folder_dir": "Walk_R0_003",})