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424 lines
18 KiB
Python
424 lines
18 KiB
Python
import math
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import random
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import time
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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 U
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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 sprint(gym.Env):
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def __init__(self, ip, server_p, monitor_p, r_type, enable_draw) -> 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.bias_dir = 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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self.distance = None
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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, enable_draw)
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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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# Step behavior defaults
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self.STEP_DUR = 10
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self.STEP_Z_SPAN = 0.2
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self.STEP_Z_MAX = 0.7
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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] * 2 # wider step
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sample_time = self.player.world.robot.STEPTIME
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max_ankle_z = nao_specs[5] * 1.8
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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.walk_rel_orientation = None
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self.walk_rel_target = None
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self.walk_target = None
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self.walk_distance = None
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self.act = np.zeros(16, np.float32) # memory variable
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# State space
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obs_size = 63
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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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# 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.ball_pos = np.array([0, 0, 0])
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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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self.gait: Step_Generator = self.player.behavior.get_custom_behavior_object("Walk").env.step_generator
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self.last_target_update_time = time.time()
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def observe(self, init=False):
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r = self.player.world.robot
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if init: # reset variables
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self.act = np.zeros(16, np.float32) # memory variable
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self.step_counter = 0
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# index observation naive normalization
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self.obs[0] = min(self.step_counter, 15 * 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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# Joints: Forward kinematics for ankles + feet rotation + arms (pitch + roll)
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rel_lankle = self.player.inv_kinematics.get_body_part_pos_relative_to_hip(
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"lankle") # ankle position relative to center of both hip joints
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rel_rankle = self.player.inv_kinematics.get_body_part_pos_relative_to_hip(
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"rankle") # ankle position relative to center of both hip joints
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lf = r.head_to_body_part_transform("torso", r.body_parts['lfoot'].transform) # foot transform relative to torso
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rf = r.head_to_body_part_transform("torso", r.body_parts['rfoot'].transform) # foot transform relative to torso
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lf_rot_rel_torso = np.array(
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[lf.get_roll_deg(), lf.get_pitch_deg(), lf.get_yaw_deg()]) # foot rotation relative to torso
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rf_rot_rel_torso = np.array(
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[rf.get_roll_deg(), rf.get_pitch_deg(), rf.get_yaw_deg()]) # foot rotation relative to torso
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# pose
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self.obs[23:26] = rel_lankle * (8, 8, 5)
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self.obs[26:29] = rel_rankle * (8, 8, 5)
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self.obs[29:32] = lf_rot_rel_torso / 20
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self.obs[32:35] = rf_rot_rel_torso / 20
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self.obs[35:39] = r.joints_position[14:18] / 100 # arms (pitch + roll)
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# velocity
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self.obs[39:55] = r.joints_target_last_speed[2:18] # predictions == last action
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'''
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Expected observations for walking state:
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Time step R 0 1 2 3 4 5 6 7 0
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Progress 1 0 .14 .28 .43 .57 .71 .86 1 0
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Left leg active T F F F F F F F F T
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'''
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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[55] = 1 # step progress
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self.obs[56] = 1 # 1 if left leg is active
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self.obs[57] = 0 # 1 if right leg is active
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else:
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self.obs[55] = self.step_generator.external_progress # step progress
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self.obs[56] = float(self.step_generator.state_is_left_active) # 1 if left leg is active
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self.obs[57] = float(not self.step_generator.state_is_left_active) # 1 if right leg is active
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'''
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Create internal target with a smoother variation
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'''
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MAX_LINEAR_DIST = 0.5
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MAX_LINEAR_DIFF = 0.014 # max difference (meters) per step
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MAX_ROTATION_DIFF = 1.6 # max difference (degrees) per step
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MAX_ROTATION_DIST = 45
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if init:
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self.internal_rel_orientation = 0
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self.internal_target = np.zeros(2)
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previous_internal_target = np.copy(self.internal_target)
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# ---------------------------------------------------------------- compute internal linear target
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rel_raw_target_size = np.linalg.norm(self.walk_rel_target)
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if rel_raw_target_size == 0:
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rel_target = self.walk_rel_target
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else:
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rel_target = self.walk_rel_target / rel_raw_target_size * min(self.walk_distance, MAX_LINEAR_DIST)
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internal_diff = rel_target - self.internal_target
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internal_diff_size = np.linalg.norm(internal_diff)
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if internal_diff_size > MAX_LINEAR_DIFF:
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self.internal_target += internal_diff * (MAX_LINEAR_DIFF / internal_diff_size)
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else:
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self.internal_target[:] = rel_target
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# ---------------------------------------------------------------- compute internal rotation target
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internal_ori_diff = np.clip(M.normalize_deg(self.walk_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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internal_target_vel = self.internal_target - previous_internal_target
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self.obs[58] = self.internal_target[0] / MAX_LINEAR_DIST
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self.obs[59] = self.internal_target[1] / MAX_LINEAR_DIST
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self.obs[60] = self.internal_rel_orientation / MAX_ROTATION_DIST
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self.obs[61] = internal_target_vel[0] / MAX_LINEAR_DIFF
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self.obs[62] = internal_target_vel[0] / MAX_LINEAR_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.player.scom.unofficial_set_play_mode("PlayOn")
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Gen_ball_pos = [15, 0, 0]
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self.Gen_player_pos = (random.random() * 3 - 15, 10 - random.random() * 20, 0.5)
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self.ball_pos = np.array(Gen_ball_pos)
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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.generate_random_target(self.Gen_player_pos[:2])
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distance = np.linalg.norm(self.walk_target[:2] - self.Gen_player_pos[:2])
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self.walk_distance = distance
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self.walk_rel_target = M.rotate_2d_vec(
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(self.walk_target[0] - r.loc_head_position[0], self.walk_target[1] - r.loc_head_position[1]),
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-r.imu_torso_orientation)
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self.walk_rel_orientation = M.vector_angle(self.walk_rel_target)
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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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if w.time_local_ms - self.reset_time > 2500 and not self.gait.state_is_left_active and self.gait.state_current_ts == 2:
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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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self.behavior.execute_sub_behavior("Walk", reset_walk, self.walk_rel_target, True, None, True,
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None) # 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 generate_random_target(self, position, x_range=(-15, 15), y_range=(-10, 10)):
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while True:
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x = np.random.uniform(x_range[0], x_range[1])
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y = np.random.uniform(y_range[0], y_range[1])
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if np.linalg.norm(np.array([x, y]) - position) >= 10:
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break
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self.walk_target = np.array([x, y])
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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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internal_dist = np.linalg.norm(self.internal_target)
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action_mult = 1 if internal_dist > 0.2 else (0.7 / 0.2) * internal_dist + 0.3
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self.walk_rel_target = M.rotate_2d_vec(
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(self.walk_target[0] - r.loc_head_position[0], self.walk_target[1] - r.loc_head_position[1]), -r.imu_torso_orientation)
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self.distance = np.linalg.norm(self.walk_target - r.loc_head_position[:2])
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self.walk_distance = np.linalg.norm(self.walk_rel_target)
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if self.distance <= 0.5:
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self.generate_random_target(r.loc_head_position[:2])
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self.walk_rel_target = M.rotate_2d_vec(
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(self.walk_target[0] - r.loc_head_position[0], self.walk_target[1] - r.loc_head_position[1]),
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-r.imu_torso_orientation)
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self.distance = np.linalg.norm(self.walk_target - r.loc_head_position[:2])
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self.walk_distance = np.linalg.norm(self.walk_rel_target)
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self.walk_rel_orientation = M.vector_angle(self.walk_rel_target) * 0.3
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# exponential moving average
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self.act = 0.8 * self.act + 0.2 * action * action_mult * 0.7
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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] * 0.02, max(0.01, a[1] * 0.02 + lfy), a[2] * 0.01 + lfz) # limit y to avoid self collision
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r_ankle_pos = (a[3] * 0.02, min(a[4] * 0.02 + rfy, -0.01), a[5] * 0.01 + rfz) # limit y to avoid self collision
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l_foot_rot = a[6:9] * (3, 3, 5)
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r_foot_rot = a[9:12] * (3, 3, 5)
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# Limit leg yaw/pitch
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l_foot_rot[2] = max(0, l_foot_rot[2] + 7)
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r_foot_rot[2] = min(0, r_foot_rot[2] - 7)
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# Arms actions
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arms = np.copy(self.DEFAULT_ARMS) # default arms pose
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arm_swing = math.sin(self.step_generator.state_current_ts / self.STEP_DUR * math.pi) * 6
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inv = 1 if self.step_generator.state_is_left_active else -1
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arms[0:4] += a[12:16] * 4 + (-arm_swing * inv, arm_swing * inv, 0, 0) # 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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obs = self.observe()
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robot_speed = np.linalg.norm(r.loc_torso_velocity[:2])
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direction_error = abs(self.walk_rel_orientation)
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direction_error = min(direction_error, 10)
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reward = robot_speed * (1 - direction_error / 10) * 0.6
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if self.distance < 0.5:
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reward += 10
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if self.player.behavior.is_ready("Get_Up"):
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self.terminal = True
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else:
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self.terminal = False
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return obs, reward, self.terminal, {}
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class Train(Train_Base):
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def __init__(self, script) -> None:
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super().__init__(script)
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def train(self, args):
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# --------------------------------------- Learning parameters
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n_envs = min(12, os.cpu_count())
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n_steps_per_env = 1024 # RolloutBuffer is of size (n_steps_per_env * n_envs)
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minibatch_size = 64 # should be a factor of (n_steps_per_env * n_envs)
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total_steps = 50000000
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learning_rate = 3e-4
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folder_name = f'Sprint_R{self.robot_type}'
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model_path = f'./scripts/gyms/logs/{folder_name}/'
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# print("Model path:", model_path)
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# --------------------------------------- Run algorithm
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def init_env(i_env):
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def thunk():
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return sprint(self.ip, self.server_p + i_env, self.monitor_p_1000 + i_env, self.robot_type, False)
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return thunk
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servers = Server(self.server_p, self.monitor_p_1000, n_envs + 1) # include 1 extra server for testing
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env = SubprocVecEnv([init_env(i) for i in range(n_envs)])
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eval_env = SubprocVecEnv([init_env(n_envs)])
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try:
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if "model_file" in args: # retrain
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model = PPO.load(args["model_file"], env=env, device="cuda", n_envs=n_envs, n_steps=n_steps_per_env,
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batch_size=minibatch_size, learning_rate=learning_rate)
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else: # train new model
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model = PPO("MlpPolicy", env=env, verbose=1, n_steps=n_steps_per_env, batch_size=minibatch_size,
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learning_rate=learning_rate, device="cuda")
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model_path = self.learn_model(model, total_steps, model_path, eval_env=eval_env,
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eval_freq=n_steps_per_env * 20, save_freq=n_steps_per_env * 200,
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backup_env_file=__file__)
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except KeyboardInterrupt:
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sleep(1) # wait for child processes
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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 = Server(self.server_p - 1, self.monitor_p, 1)
|
|
env = sprint(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() |