diff --git a/scripts/gyms/Walk.py b/scripts/gyms/Walk.py new file mode 100644 index 0000000..d30214f --- /dev/null +++ b/scripts/gyms/Walk.py @@ -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",})