new mimic
parent
6c9467f21e
commit
7d49579686
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import pickle
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# 加载并查看 pkl 文件
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def load_and_display_observations(file_path):
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with open(file_path, 'rb') as f:
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observations = pickle.load(f)
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# 打印观测值
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for i, obs in enumerate(observations):
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print(f"Observation {i + 1}: {obs}")
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print(type(observations))
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# 使用函数查看指定的 pkl 文件
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load_and_display_observations('scripts/gyms/dribble_expert_data.pkl')
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import math
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import pickle
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import random
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from agent.Base_Agent import Base_Agent as Agent
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from behaviors.custom.Step.Step import Step
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from world.commons.Draw import Draw
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import SubprocVecEnv
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from scripts.commons.Server import Server
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from scripts.commons.Train_Base import Train_Base
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from time import sleep
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import os, gym
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import numpy as np
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from math_ops.Math_Ops import Math_Ops as M
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from behaviors.custom.Step.Step_Generator import Step_Generator
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'''
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Objective:
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Learn how to run forward using step primitive
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----------
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- class Basic_Run: implements an OpenAI custom gym
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- class Train: implements algorithms to train a new model or test an existing model
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'''
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class dribble(gym.Env):
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def __init__(self, ip, server_p, monitor_p, r_type, enable_draw) -> None:
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self.abs_ori = 75
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self.ball_dist_hip_center_2d = 0
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self.ball_dist_hip_center = None
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self.internal_rel_orientation = None
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self.dribble_speed = 1
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self.gym_last_internal_abs_ori = None
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self.internal_target_vel = None
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self.Gen_player_pos = None
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self.internal_target = None
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self.values_l = None
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self.values_r = None
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self.reset_time = None
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self.behavior = None
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self.robot_type = r_type
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self.kick_ori = 0
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self.terminal = False
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# Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
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self.player = Agent(ip, server_p, monitor_p, 1, self.robot_type, "Gym", True, True)
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self.step_counter = 0 # to limit episode size
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self.ik = self.player.inv_kinematics
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self.dribble_rel_orientation = 0 # relative to imu_torso_orientation (in degrees)
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# Step behavior defaults
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self.STEP_DUR = 8
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self.STEP_Z_SPAN = 0.02
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self.STEP_Z_MAX = 0.70
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nao_specs = self.ik.NAO_SPECS
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self.leg_length = nao_specs[1] + nao_specs[3] # upper leg height + lower leg height
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feet_y_dev = nao_specs[0] * 1.12 # wider step
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sample_time = self.player.world.robot.STEPTIME
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max_ankle_z = nao_specs[5]
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self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z)
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self.DEFAULT_ARMS = np.array([-90, -90, 8, 8, 90, 90, 70, 70], np.float32)
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self.act = np.zeros(16, np.float32) # memory variable
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self.path_manager = self.player.path_manager
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# State space
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obs_size = 76
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self.obs = np.zeros(obs_size, np.float32)
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self.observation_space = gym.spaces.Box(low=np.full(obs_size, -np.inf, np.float32),
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high=np.full(obs_size, np.inf, np.float32), dtype=np.float32)
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self.ball_x_center = 0.21
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self.ball_y_center = -0.045
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# Action space
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MAX = np.finfo(np.float32).max
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self.no_of_actions = act_size = 16
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self.action_space = gym.spaces.Box(low=np.full(act_size, -MAX, np.float32),
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high=np.full(act_size, MAX, np.float32), dtype=np.float32)
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# Place ball far away to keep landmarks in FoV (head follows ball while using Step behavior)
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self.player.scom.unofficial_move_ball((14, 0, 0.042))
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self.player.scom.unofficial_set_play_mode("PlayOn")
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self.player.scom.unofficial_move_ball((0, 0, 0))
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with open("/home/her-darling/APOLLO_PRO/TEST_Dribble/scripts/gyms/dribble_observations.pkl", 'rb') as f:
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self.obs_list = pickle.load(f)
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self.obs_list = np.array(self.obs_list)
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def observe(self, init=False, virtual_ball=False):
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r = self.player.world.robot
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w = self.player.world
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if init: # reset variables
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self.step_counter = 0
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self.act = np.zeros(16, np.float32) # memory variable
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# index observation naive normalization
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self.obs[0] = min(self.step_counter, 12 * 8) / 100 # simple counter: 0,1,2,3...
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self.obs[1] = r.loc_head_z * 3 # z coordinate (torso)
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self.obs[2] = r.loc_head_z_vel / 2 # z velocity (torso)
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self.obs[3] = r.imu_torso_roll / 15 # absolute torso roll in deg
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self.obs[4] = r.imu_torso_pitch / 15 # absolute torso pitch in deg
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self.obs[5:8] = r.gyro / 100 # gyroscope
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self.obs[8:11] = r.acc / 10 # accelerometer
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self.obs[11:17] = r.frp.get('lf', np.zeros(6)) * (10, 10, 10, 0.01, 0.01,
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0.01) # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
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self.obs[17:23] = r.frp.get('rf', np.zeros(6)) * (10, 10, 10, 0.01, 0.01,
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0.01) # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
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# *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0)
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self.obs[23:43] = r.joints_position[2:22] / 100 # position of all joints except head & toes (for robot type 4)
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self.obs[43:63] = r.joints_speed[2:22] / 6.1395 # speed of all joints except head & toes (for robot type 4)
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if init: # the walking parameters refer to the last parameters in effect (after a reset, they are pointless)
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self.obs[63] = 1 # step progress
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self.obs[64] = 1 # 1 if left leg is active
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self.obs[65] = 0 # 1 if right leg is active
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self.obs[66] = 0
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else:
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self.obs[63] = self.step_generator.external_progress # step progress
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self.obs[64] = float(self.step_generator.state_is_left_active) # 1 if left leg is active
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self.obs[65] = float(not self.step_generator.state_is_left_active) # 1 if right leg is active
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self.obs[66] = math.sin(self.step_generator.state_current_ts / self.step_generator.ts_per_step * math.pi)
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# Ball
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ball_rel_hip_center = self.ik.torso_to_hip_transform(w.ball_rel_torso_cart_pos)
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self.ball_dist_hip_center = np.linalg.norm(ball_rel_hip_center)
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ball_rel_torso_xy = w.ball_rel_torso_cart_pos[:2] # 取X和Y分量
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self.ball_dist_hip_center_2d = np.linalg.norm(ball_rel_torso_xy)
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if init:
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self.obs[67:70] = (0, 0, 0) # Initial velocity is 0
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elif w.ball_is_visible:
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self.obs[67:70] = (ball_rel_hip_center - self.obs[
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70:73]) * 10 # Ball velocity, relative to ankle's midpoint
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self.obs[70:73] = ball_rel_hip_center # Ball position, relative to hip
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self.obs[73] = self.ball_dist_hip_center * 2
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if virtual_ball: # simulate the ball between the robot's feet
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self.obs[67:74] = (0, 0, 0, 0.05, 0, -0.175, 0.36)
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'''
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Create internal target with a smoother variation
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'''
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MAX_ROTATION_DIFF = 20 # max difference (degrees) per visual step
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MAX_ROTATION_DIST = 80
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if init:
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self.internal_rel_orientation = 0
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self.internal_target_vel = 0
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self.gym_last_internal_abs_ori = r.imu_torso_orientation # for training purposes (reward)
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# ---------------------------------------------------------------- compute internal target
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if w.vision_is_up_to_date:
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previous_internal_rel_orientation = np.copy(self.internal_rel_orientation)
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internal_ori_diff = np.clip(M.normalize_deg(self.dribble_rel_orientation - self.internal_rel_orientation),
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-MAX_ROTATION_DIFF, MAX_ROTATION_DIFF)
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self.internal_rel_orientation = np.clip(M.normalize_deg(self.internal_rel_orientation + internal_ori_diff),
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-MAX_ROTATION_DIST, MAX_ROTATION_DIST)
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# Observations
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self.internal_target_vel = self.internal_rel_orientation - previous_internal_rel_orientation
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self.gym_last_internal_abs_ori = self.internal_rel_orientation + r.imu_torso_orientation
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# ----------------------------------------------------------------- observations
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self.obs[74] = self.internal_rel_orientation / MAX_ROTATION_DIST
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self.obs[75] = self.internal_target_vel / MAX_ROTATION_DIFF
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return self.obs
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def execute_ik(self, l_pos, l_rot, r_pos, r_rot):
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r = self.player.world.robot
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# Apply IK to each leg + Set joint targets
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# Left leg
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indices, self.values_l, error_codes = self.ik.leg(l_pos, l_rot, True, dynamic_pose=False)
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r.set_joints_target_position_direct(indices, self.values_l, harmonize=False)
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# Right leg
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indices, self.values_r, error_codes = self.ik.leg(r_pos, r_rot, False, dynamic_pose=False)
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r.set_joints_target_position_direct(indices, self.values_r, harmonize=False)
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def sync(self):
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''' Run a single simulation step '''
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r = self.player.world.robot
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self.player.scom.commit_and_send(r.get_command())
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self.player.scom.receive()
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def reset(self):
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# print("reset")
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'''
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Reset and stabilize the robot
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Note: for some behaviors it would be better to reduce stabilization or add noise
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'''
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self.abs_ori = 45
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self.player.scom.unofficial_set_play_mode("PlayOn")
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Gen_ball_pos = [- 9, 0, 0]
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self.Gen_player_pos = (Gen_ball_pos[0] - 1.5, Gen_ball_pos[1], 0.5)
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self.player.scom.unofficial_move_ball((Gen_ball_pos[0], Gen_ball_pos[1], Gen_ball_pos[2]))
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self.step_counter = 0
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self.behavior = self.player.behavior
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r = self.player.world.robot
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w = self.player.world
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t = w.time_local_ms
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self.reset_time = t
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self.b_rel = w.ball_rel_torso_cart_pos[:2]
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self.path_manager = self.player.path_manager
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for _ in range(25):
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self.player.scom.unofficial_beam(self.Gen_player_pos, 0) # beam player continuously (floating above ground)
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self.player.behavior.execute("Zero_Bent_Knees")
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self.sync()
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# beam player to ground
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self.player.scom.unofficial_beam(self.Gen_player_pos, 0)
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r.joints_target_speed[
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0] = 0.01 # move head to trigger physics update (rcssserver3d bug when no joint is moving)
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self.sync()
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# stabilize on ground
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for _ in range(7):
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self.player.behavior.execute("Zero_Bent_Knees")
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self.sync()
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# walk to ball
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while True:
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self.b_rel = w.ball_rel_torso_cart_pos[:2]
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if self.player.behavior.is_ready("Get_Up"):
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self.player.behavior.execute_to_completion("Get_Up")
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if 0.26 > self.b_rel[0] > 0.18 and abs(self.b_rel[1]) < 0.04 and w.ball_is_visible:
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break
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else:
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if self.player.behavior.is_ready("Get_Up"):
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self.player.behavior.execute_to_completion("Get_Up")
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reset_walk = self.behavior.previous_behavior != "Walk" # reset walk if it wasn't the previous behavior
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rel_target = self.b_rel + (-0.23, 0) # relative target is a circle (center: ball, radius:0.23m)
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rel_ori = M.vector_angle(self.b_rel) # relative orientation of ball, NOT the target!
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dist = max(0.08, np.linalg.norm(rel_target) * 0.7) # slow approach
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self.behavior.execute_sub_behavior("Walk", reset_walk, rel_target, False, rel_ori, False,
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dist) # target, is_target_abs, ori, is_ori_abs, distance
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self.sync()
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self.act = np.zeros(self.no_of_actions, np.float32)
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return self.observe(True)
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def render(self, mode='human', close=False):
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return
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def close(self):
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Draw.clear_all()
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self.player.terminate()
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def step(self, action):
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r = (self.
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player.world.robot)
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w = self.player.world
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d = w.draw
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if w.ball_abs_pos[1] > 0: #
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dribble_target = (15, 5)
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else:
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dribble_target = (15, -5)
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self.dribble_rel_orientation = self.path_manager.get_dribble_path(optional_2d_target=dribble_target)[1]
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# exponential moving average
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self.act = 0.85 * self.act + 0.15 * action * 0.7 * 0.95 * self.dribble_speed
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# execute Step behavior to extract the target positions of each leg (we will override these targets)
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lfy, lfz, rfy, rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR,
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self.STEP_Z_SPAN,
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self.leg_length * self.STEP_Z_MAX)
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# Leg IK
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a = self.act
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l_ankle_pos = (a[0] * 0.025 - 0.01, a[1] * 0.01 + lfy, a[2] * 0.01 + lfz)
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r_ankle_pos = (a[3] * 0.025 - 0.01, a[4] * 0.01 + rfy, a[5] * 0.01 + rfz)
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l_foot_rot = a[6:9] * (2, 2, 3)
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r_foot_rot = a[9:12] * (2, 2, 3)
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# Limit leg yaw/pitch (and add bias)
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l_foot_rot[2] = max(0, l_foot_rot[2] + 18.3)
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r_foot_rot[2] = min(0, r_foot_rot[2] - 18.3)
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# Arms actions
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arms = np.copy(self.DEFAULT_ARMS) # default arms pose
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arms[0:4] += a[12:16] * 4 # arms pitch+roll
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# Set target positions
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self.execute_ik(l_ankle_pos, l_foot_rot, r_ankle_pos, r_foot_rot) # legs
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r.set_joints_target_position_direct(slice(14, 22), arms, harmonize=False) # arms
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self.sync()
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self.step_counter += 1
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# 收集观测
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obs = self.observe()
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angle_rad = np.radians(self.gym_last_internal_abs_ori) # 将角度转换为弧度
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unit_vector = np.array([np.cos(angle_rad), np.sin(angle_rad)]) # 计算单位向量
|
||||||
|
if np.linalg.norm(w.ball_cheat_abs_vel[:2]) != 0:
|
||||||
|
cos_theta = np.dot(unit_vector, w.ball_cheat_abs_vel[:2]) / (
|
||||||
|
np.linalg.norm(unit_vector) * np.linalg.norm(w.ball_cheat_abs_vel[:2]))
|
||||||
|
else:
|
||||||
|
cos_theta = 0
|
||||||
|
# 计算奖励
|
||||||
|
ref = self.obs_list[self.step_counter + 3]
|
||||||
|
loss = self.cal_reward(ref, self.obs) * 0.075
|
||||||
|
reward = np.linalg.norm(w.ball_cheat_abs_vel) * cos_theta + loss
|
||||||
|
|
||||||
|
if self.ball_dist_hip_center_2d < 0.115:
|
||||||
|
reward = 0
|
||||||
|
# 判断终止
|
||||||
|
if self.player.behavior.is_ready("Get_Up"):
|
||||||
|
self.terminal = True
|
||||||
|
elif w.time_local_ms - self.reset_time > 7500 * 3 or np.linalg.norm(
|
||||||
|
w.ball_cheat_abs_pos[:2] - r.loc_head_position[:2]) > 0.45 or not w.is_ball_abs_pos_from_vision:
|
||||||
|
self.terminal = True
|
||||||
|
else:
|
||||||
|
self.terminal = False
|
||||||
|
return obs, reward, self.terminal, {}
|
||||||
|
|
||||||
|
def cal_reward(self,ref,obs):
|
||||||
|
reward = -0.5*np.linalg.norm(ref[23:39]*100 - obs[23:39]*100) -0.5*np.linalg.norm(ref[1:23]*30 - obs[1:23]*30)
|
||||||
|
|
||||||
|
reward -= (2*np.linalg.norm(ref[20:23] - obs[20:23]) + 2*np.linalg.norm(ref[14:17] - obs[14:17]))
|
||||||
|
return reward
|
||||||
|
|
||||||
|
class Train(Train_Base):
|
||||||
|
def __init__(self, script) -> None:
|
||||||
|
super().__init__(script)
|
||||||
|
|
||||||
|
def train(self, args):
|
||||||
|
|
||||||
|
# --------------------------------------- Learning parameters
|
||||||
|
n_envs = min(12, os.cpu_count())
|
||||||
|
n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs)
|
||||||
|
minibatch_size = 64 # should be a factor of (n_steps_per_env * n_envs)
|
||||||
|
total_steps = 50000000
|
||||||
|
learning_rate = 3e-4
|
||||||
|
folder_name = f'Sprint_mimic_R{self.robot_type}'
|
||||||
|
model_path = f'./scripts/gyms/logs/{folder_name}/'
|
||||||
|
|
||||||
|
# print("Model path:", model_path)
|
||||||
|
|
||||||
|
# --------------------------------------- Run algorithm
|
||||||
|
def init_env(i_env):
|
||||||
|
def thunk():
|
||||||
|
return dribble(self.ip, self.server_p + i_env, self.monitor_p_1000 + i_env, self.robot_type, False)
|
||||||
|
|
||||||
|
return thunk
|
||||||
|
|
||||||
|
servers = Server(self.server_p, self.monitor_p_1000, n_envs + 1) # include 1 extra server for testing
|
||||||
|
|
||||||
|
env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
|
||||||
|
eval_env = SubprocVecEnv([init_env(n_envs)])
|
||||||
|
|
||||||
|
try:
|
||||||
|
if "model_file" in args: # retrain
|
||||||
|
model = PPO.load(args["model_file"], env=env, device="cuda", 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="cuda")
|
||||||
|
|
||||||
|
model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
|
||||||
|
eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 20,
|
||||||
|
backup_env_file=__file__)
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
sleep(1) # wait for child processes
|
||||||
|
("\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 = Server(self.server_p - 1, self.monitor_p, 1)
|
||||||
|
env = dribble(self.ip, self.server_p - 1, self.monitor_p, self.robot_type, True)
|
||||||
|
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()
|
||||||
Loading…
Reference in New Issue