Dribble/scripts/gyms/Sprint.py

import math
import random

from agent.Base_Agent import Base_Agent as Agent
from behaviors.custom.Step.Step import Step
from world.commons.Draw import Draw
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import SubprocVecEnv
from scripts.commons.Server import Server
from scripts.commons.Train_Base import Train_Base
from time import sleep
import os, gym
import numpy as np
from math_ops.Math_Ops import Math_Ops as U
from math_ops.Math_Ops import Math_Ops as M
from behaviors.custom.Step.Step_Generator import Step_Generator

'''
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 sprint(gym.Env):
    def __init__(self, ip, server_p, monitor_p, r_type, enable_draw) -> None:

        self.Gen_player_pos = None
        self.internal_target = None
        self.values_l = None
        self.values_r = None
        self.reset_time = None
        self.behavior = None
        self.bias_dir = None
        self.robot_type = r_type
        self.kick_ori = 0
        self.terminal = False
        # Args: Server IP, Agent Port, Monitor Port, Uniform No., Robot Type, Team Name, Enable Log, Enable Draw
        self.player = Agent(ip, server_p, monitor_p, 1, self.robot_type, "Gym", True, enable_draw)
        self.step_counter = 0  # to limit episode size
        self.ik = self.player.inv_kinematics
        self.target = None

        # Step behavior defaults
        self.STEP_DUR = 10
        self.STEP_Z_SPAN = 0.2
        self.STEP_Z_MAX = 0.7
        nao_specs = self.ik.NAO_SPECS
        self.leg_length = nao_specs[1] + nao_specs[3]  # upper leg height + lower leg height
        feet_y_dev = nao_specs[0] * 2  # wider step
        sample_time = self.player.world.robot.STEPTIME
        max_ankle_z = nao_specs[5] * 1.8
        self.step_generator = Step_Generator(feet_y_dev, sample_time, max_ankle_z)
        self.DEFAULT_ARMS = np.array([-90, -90, 8, 8, 90, 90, 70, 70], np.float32)

        self.walk_rel_orientation = None
        self.walk_rel_target = None
        self.walk_distance = None
        self.act = np.zeros(16, np.float32)  # memory variable

        # State space
        obs_size = 63
        self.obs = np.zeros(obs_size, np.float32)
        self.observation_space = gym.spaces.Box(low=np.full(obs_size, -np.inf, np.float32),
                                                high=np.full(obs_size, np.inf, np.float32), dtype=np.float32)

        # Action space
        MAX = np.finfo(np.float32).max
        self.no_of_actions = act_size = 16
        self.action_space = gym.spaces.Box(low=np.full(act_size, -MAX, np.float32),
                                           high=np.full(act_size, MAX, np.float32), dtype=np.float32)

        # Place ball far away to keep landmarks in FoV (head follows ball while using Step behavior)
        self.player.scom.unofficial_move_ball((14, 0, 0.042))
        self.ball_pos = np.array([0, 0, 0])

        self.player.scom.unofficial_set_play_mode("PlayOn")
        self.player.scom.unofficial_move_ball((0, 0, 0))

        self.gait: Step_Generator = self.player.behavior.get_custom_behavior_object("Walk").env.step_generator

    def observe(self, init=False):
        r = self.player.world.robot

        if init:  # reset variables
            self.act = np.zeros(16, np.float32)  # memory variable
            self.step_counter = 0

        # index       observation              naive normalization
        self.obs[0] = min(self.step_counter, 15 * 8) / 100  # simple counter: 0,1,2,3...
        self.obs[1] = r.loc_head_z * 3  # z coordinate (torso)
        self.obs[2] = r.loc_head_z_vel / 2  # z velocity (torso)
        self.obs[3] = r.imu_torso_roll / 15  # absolute torso roll  in deg
        self.obs[4] = r.imu_torso_pitch / 15  # absolute torso pitch in deg
        self.obs[5:8] = r.gyro / 100  # gyroscope
        self.obs[8:11] = r.acc / 10  # accelerometer

        self.obs[11:17] = r.frp.get('lf', np.zeros(6)) * (10, 10, 10, 0.01, 0.01,
                                                          0.01)  # left foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
        self.obs[17:23] = r.frp.get('rf', np.zeros(6)) * (10, 10, 10, 0.01, 0.01,
                                                          0.01)  # right foot: relative point of origin (p) and force vector (f) -> (px,py,pz,fx,fy,fz)*
        # *if foot is not touching the ground, then (px=0,py=0,pz=0,fx=0,fy=0,fz=0)

        # Joints: Forward kinematics for ankles + feet rotation + arms (pitch + roll)
        rel_lankle = self.player.inv_kinematics.get_body_part_pos_relative_to_hip(
            "lankle")  # ankle position relative to center of both hip joints
        rel_rankle = self.player.inv_kinematics.get_body_part_pos_relative_to_hip(
            "rankle")  # ankle position relative to center of both hip joints
        lf = r.head_to_body_part_transform("torso", r.body_parts['lfoot'].transform)  # foot transform relative to torso
        rf = r.head_to_body_part_transform("torso", r.body_parts['rfoot'].transform)  # foot transform relative to torso
        lf_rot_rel_torso = np.array(
            [lf.get_roll_deg(), lf.get_pitch_deg(), lf.get_yaw_deg()])  # foot rotation relative to torso
        rf_rot_rel_torso = np.array(
            [rf.get_roll_deg(), rf.get_pitch_deg(), rf.get_yaw_deg()])  # foot rotation relative to torso

        # pose
        self.obs[23:26] = rel_lankle * (8, 8, 5)
        self.obs[26:29] = rel_rankle * (8, 8, 5)
        self.obs[29:32] = lf_rot_rel_torso / 20
        self.obs[32:35] = rf_rot_rel_torso / 20
        self.obs[35:39] = r.joints_position[14:18] / 100  # arms (pitch + roll)

        # velocity
        self.obs[39:55] = r.joints_target_last_speed[2:18]  # predictions == last action

        '''
        Expected observations for walking state:
        Time step        R  0   1   2   3   4   5   6   7   0
        Progress         1  0 .14 .28 .43 .57 .71 .86   1   0
        Left leg active  T  F   F   F   F   F   F   F   F   T
        '''

        if init:  # the walking parameters refer to the last parameters in effect (after a reset, they are pointless)
            self.obs[55] = 1  # step progress
            self.obs[56] = 1  # 1 if left  leg is active
            self.obs[57] = 0  # 1 if right leg is active
        else:
            self.obs[55] = self.step_generator.external_progress  # step progress
            self.obs[56] = float(self.step_generator.state_is_left_active)  # 1 if left  leg is active
            self.obs[57] = float(not self.step_generator.state_is_left_active)  # 1 if right leg is active

        '''
        Create internal target with a smoother variation
        '''

        MAX_LINEAR_DIST = 0.5
        MAX_LINEAR_DIFF = 0.014  # max difference (meters) per step
        MAX_ROTATION_DIFF = 1.6  # max difference (degrees) per step
        MAX_ROTATION_DIST = 45

        if init:
            self.internal_rel_orientation = 0
            self.internal_target = np.zeros(2)

        previous_internal_target = np.copy(self.internal_target)

        # ---------------------------------------------------------------- compute internal linear target

        rel_raw_target_size = np.linalg.norm(self.walk_rel_target)

        if rel_raw_target_size == 0:
            rel_target = self.walk_rel_target
        else:
            rel_target = self.walk_rel_target / rel_raw_target_size * min(self.walk_distance, MAX_LINEAR_DIST)

        internal_diff = rel_target - self.internal_target
        internal_diff_size = np.linalg.norm(internal_diff)

        if internal_diff_size > MAX_LINEAR_DIFF:
            self.internal_target += internal_diff * (MAX_LINEAR_DIFF / internal_diff_size)
        else:
            self.internal_target[:] = rel_target

        # ---------------------------------------------------------------- compute internal rotation target

        internal_ori_diff = np.clip(M.normalize_deg(self.walk_rel_orientation - self.internal_rel_orientation),
                                    -MAX_ROTATION_DIFF, MAX_ROTATION_DIFF)
        self.internal_rel_orientation = np.clip(M.normalize_deg(self.internal_rel_orientation + internal_ori_diff),
                                                -MAX_ROTATION_DIST, MAX_ROTATION_DIST)

        # ----------------------------------------------------------------- observations

        internal_target_vel = self.internal_target - previous_internal_target

        self.obs[58] = self.internal_target[0] / MAX_LINEAR_DIST
        self.obs[59] = self.internal_target[1] / MAX_LINEAR_DIST
        self.obs[60] = self.internal_rel_orientation / MAX_ROTATION_DIST
        self.obs[61] = internal_target_vel[0] / MAX_LINEAR_DIFF
        self.obs[62] = internal_target_vel[0] / MAX_LINEAR_DIFF

        return self.obs

    def execute_ik(self, l_pos, l_rot, r_pos, r_rot):
        r = self.player.world.robot
        # Apply IK to each leg + Set joint targets

        # Left leg
        indices, self.values_l, error_codes = (self.ik.leg(l_pos, l_rot, True, dynamic_pose=False))

        r.set_joints_target_position_direct(indices, self.values_l, harmonize=False)

        # Right leg
        indices, self.values_r, error_codes = self.ik.leg(r_pos, r_rot, False, dynamic_pose=False)

        r.set_joints_target_position_direct(indices, self.values_r, harmonize=False)

    def sync(self):
        ''' Run a single simulation step '''
        r = self.player.world.robot
        self.player.scom.commit_and_send(r.get_command())
        self.player.scom.receive()

    def reset(self):
        # print("reset")
        '''
        Reset and stabilize the robot
        Note: for some behaviors it would be better to reduce stabilization or add noise
        '''

        self.player.scom.unofficial_set_play_mode("PlayOn")

        Gen_ball_pos = [15, 0, 0]
        self.Gen_player_pos = (random.random() * 3 - 15, 10 - random.random() * 20, 0.5)

        self.ball_pos = np.array(Gen_ball_pos)
        self.player.scom.unofficial_move_ball((Gen_ball_pos[0], Gen_ball_pos[1], Gen_ball_pos[2]))
        self.step_counter = 0
        self.behavior = self.player.behavior
        r = self.player.world.robot
        w = self.player.world
        t = w.time_local_ms
        self.reset_time = t

        self.generate_random_target(self.Gen_player_pos)
        distance = np.linalg.norm(self.target - self.Gen_player_pos[:2])
        self.walk_rel_target = self.target - self.Gen_player_pos[:2]
        self.walk_distance = distance
        self.walk_rel_orientation = M.vector_angle(self.walk_rel_target)

        for _ in range(25):
            self.player.scom.unofficial_beam(self.Gen_player_pos, 0)  # beam player continuously (floating above ground)
            self.player.behavior.execute("Zero_Bent_Knees")
            self.sync()

        # beam player to ground
        self.player.scom.unofficial_beam(self.Gen_player_pos, 0)
        r.joints_target_speed[
            0] = 0.01  # move head to trigger physics update (rcssserver3d bug when no joint is moving)
        self.sync()

        # stabilize on ground
        for _ in range(7):
            self.player.behavior.execute("Zero_Bent_Knees")
            self.sync()

        # walk to ball
        while True:

            if w.time_local_ms - self.reset_time > 2500 and not self.gait.state_is_left_active and self.gait.state_current_ts == 2:
                break
            else:
                if self.player.behavior.is_ready("Get_Up"):
                    self.player.behavior.execute_to_completion("Get_Up")

                reset_walk = self.behavior.previous_behavior != "Walk"  # reset walk if it wasn't the previous behavior
                self.behavior.execute_sub_behavior("Walk", reset_walk, self.walk_rel_target, True, None, True,
                                                   None)  # target, is_target_abs, ori, is_ori_abs, distance

                self.sync()

        self.act = np.zeros(self.no_of_actions, np.float32)
        return self.observe(True)

    def render(self, mode='human', close=False):
        return

    def close(self):
        Draw.clear_all()
        self.player.terminate()

    def generate_random_target(self, position, x_range=(-15, 15), y_range=(-10, 10)):
        while True:
            x = np.random.uniform(x_range[0], x_range[1])
            y = np.random.uniform(y_range[0], y_range[1])

            if np.linalg.norm(np.array([x, y]) - position[:2]) >= 10:
                break
        self.target = np.array([x, y])

    def step(self, action):

        r = (self.
             player.world.robot)

        w = self.player.world
        internal_dist = np.linalg.norm(self.internal_target)
        action_mult = 1 if internal_dist > 0.2 else (0.7 / 0.2) * internal_dist + 0.3
        self.walk_rel_target = M.rotate_2d_vec(
            (self.target[0] - r.loc_head_position[0], self.target[1] - r.loc_head_position[1]), -r.imu_torso_orientation)
        self.walk_distance = np.linalg.norm(self.walk_rel_target)
        self.walk_rel_orientation = M.vector_angle(self.walk_rel_target) * 0.3
        if self.walk_distance <= 0.5:
            self.generate_random_target(r.loc_head_position)
            self.walk_rel_target = M.rotate_2d_vec(
                (self.target[0] - r.loc_head_position[0], self.target[1] - r.loc_head_position[1]),
                -r.imu_torso_orientation)
            self.walk_distance = np.linalg.norm(self.walk_rel_target)
            self.walk_rel_orientation = M.vector_angle(self.walk_rel_target) * 0.3

        # exponential moving average
        self.act = 0.8 * self.act + 0.2 * action * action_mult * 0.7

        # execute Step behavior to extract the target positions of each leg (we will override these targets)
        lfy, lfz, rfy, rfz = self.step_generator.get_target_positions(self.step_counter == 0, self.STEP_DUR,
                                                                      self.STEP_Z_SPAN,
                                                                      self.leg_length * self.STEP_Z_MAX)

        # Leg IK
        a = self.act
        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
        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
        l_foot_rot = a[6:9] * (3, 3, 5)
        r_foot_rot = a[9:12] * (3, 3, 5)

        # Limit leg yaw/pitch
        l_foot_rot[2] = max(0, l_foot_rot[2] + 7)
        r_foot_rot[2] = min(0, r_foot_rot[2] - 7)

        # Arms actions
        arms = np.copy(self.DEFAULT_ARMS)  # default arms pose
        arm_swing = math.sin(self.step_generator.state_current_ts / self.STEP_DUR * math.pi) * 6
        inv = 1 if self.step_generator.state_is_left_active else -1
        arms[0:4] += a[12:16] * 4 + (-arm_swing * inv, arm_swing * inv, 0, 0)  # arms pitch+roll

        # Set target positions
        self.execute_ik(l_ankle_pos, l_foot_rot, r_ankle_pos, r_foot_rot)  # legs
        r.set_joints_target_position_direct(slice(14, 22), arms, harmonize=False)  # arms
        self.sync()
        self.step_counter += 1
        obs = self.observe()
        velocity_rel_orientation = M.vector_angle(M.rotate_2d_vec((r.loc_torso_velocity[0], r.loc_torso_velocity[1]), r.imu_torso_orientation)) * 0.3
        direction_error = min(abs(velocity_rel_orientation - self.walk_rel_orientation), 10)
        robot_speed = np.linalg.norm(r.loc_torso_velocity[:2])
        reward = robot_speed * (1.5 - direction_error / 10)
        if self.walk_distance < 0.5:
            reward += 10

        if self.player.behavior.is_ready("Get_Up"):
            self.terminal = True
        elif w.time_local_ms - self.reset_time > 25000 * 2:
            self.terminal = True
        else:
            self.terminal = False
        return obs, reward, self.terminal, {}


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_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 sprint(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 * 200,
                                          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 = 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()