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HRL_GUI.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Aug 28 14:21:38 2022
@author: Nigel Swenson
"""
import PySimpleGUI as sg
import os
import pickle as pkl
import numpy as np
import json
import copy
from PIL import ImageGrab
# from itertools import islice
import threading
# from mojograsp.simcore.run_from_file import run_pybullet
import pathlib
'''
Data Plotter
This is based on the Demo_PNG_Viewer by PySimpleGUI
'''
def save_element_as_file(element, filename):
"""
Saves any element as an image file. Element needs to have an underlyiong Widget available (almost if not all of them do)
:param element: The element to save
:param filename: The filename to save to. The extension of the filename determines the format (jpg, png, gif, ?)
"""
widget = element.Widget
box = (widget.winfo_rootx(), widget.winfo_rooty(), widget.winfo_rootx() + widget.winfo_width(), widget.winfo_rooty() + widget.winfo_height())
grab = ImageGrab.grab(bbox=box)
grab.save(filename)
class RNNGui():
multi_rewards = ['sparse_multigoal']
slide_rewards = ['Sparse','Distance','Distance + Finger', 'Hinge Distance + Finger', 'Slope', 'Slope + Finger','SmartDistance + Finger','SmartDistance + SmartFinger','ScaledDistance + Finger','ScaledDistance+ScaledFinger', 'SFS','DFS','TripleScaled']
rotate_rewards = ["Rotation", "Rotation+Finger"]
finger_rewards = ["continuous_finger", "end_finger"]
full_task_rewards = ["full", "full+finger"]
wall_task_rewards =["full", "slide"]
def __init__(self):
self.toggle_btn_off = b'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'
self.toggle_btn_on = b'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'
self.data_dict = {'train': {'state':[],'label':[]}, 'validation':{'state':[],'label':[]}, 'test': {'state':[],'label':[]}}
# define menu layout
self.menu = [['File', ['Open Folder', 'Exit']], ['Help', ['About', ]]]
self.args = {}
self.shuffle_type = 'Episode'
self.save_path = '/'
self.expert_path = '/'
self.load_path = '/'
self.built = False
self.train_dataset, self.validation_dataset = None, None
self.single_names = ['forward','backward','left','right','forward_left','forward_right','backward_right','backward_left']
self.double_names = ['f-b', 'l-r', 'diag-up', 'diag-down']
self.alt_double_names = ['l-r','f','b']
# define layout, show and read the window
data_layout = [ [sg.Text('Model Type'), sg.OptionMenu(values=('PPO','PPO_Expert'), k='-model', default_value='PPO')],
[sg.Text('Path to Expert Data if using FD')],
[sg.Button("Browse",key='-browse-expert',button_color='DarkBlue'),sg.Text("/", key='-expert-path')],
[sg.Text('Path to Save Data')],
[sg.Button("Browse",key='-browse-save',button_color='DarkBlue'),sg.Text("/", key='-save-path')],
[sg.Text('Path to Previous Policy if Transferring')],
[sg.Button("Browse",key='-browse-load',button_color='DarkBlue'),sg.Text("/", key='-load-path')],
[sg.Text('Object'), sg.OptionMenu(values=('Cube', 'Cylinder', 'circle', 'hourglass', 'ellipse', 'square_concave', 'square', 'triangle', 'cone', 'teardrop'), k='-object', default_value='Cube')],
[sg.Text('Hands Used For Training and Testing')],
[sg.Checkbox('2v2_50.50_50.50_43',key='2v2_50.50_50.50_1.1_43',default=False),sg.Checkbox('2v2_50.50_50.50_53',key='2v2_50.50_50.50_1.1_53', default=True),sg.Checkbox('2v2_50.50_50.50_63',key='2v2_50.50_50.50_1.1_63', default=False),sg.Checkbox('2v2_50.50_50.50_73',key='2v2_50.50_50.50_1.1_73', default=False)],
[sg.Checkbox('2v2_65.35_50.50_43',key='2v2_65.35_50.50_1.1_43', default=False),sg.Checkbox('2v2_65.35_50.50_53',key='2v2_65.35_50.50_1.1_53', default=False),sg.Checkbox('2v2_65.35_50.50_63',key='2v2_65.35_50.50_1.1_63', default=False),sg.Checkbox('2v2_65.35_50.50_73',key='2v2_65.35_50.50_1.1_73', default=False)],
[sg.Checkbox('2v2_35.65_50.50_43',key='2v2_35.65_50.50_1.1_43', default=False),sg.Checkbox('2v2_35.65_50.50_53',key='2v2_35.65_50.50_1.1_53', default=False),sg.Checkbox('2v2_35.65_50.50_63',key='2v2_35.65_50.50_1.1_63', default=False),sg.Checkbox('2v2_35.65_50.50_73',key='2v2_35.65_50.50_1.1_73', default=False)],
[sg.Checkbox('2v2_65.35_65.35_43',key='2v2_65.35_65.35_1.1_43', default=False),sg.Checkbox('2v2_65.35_65.35_53',key='2v2_65.35_65.35_1.1_53', default=False),sg.Checkbox('2v2_65.35_65.35_63',key='2v2_65.35_65.35_1.1_63', default=False),sg.Checkbox('2v2_65.35_65.35_73',key='2v2_65.35_65.35_1.1_73', default=False)],
[sg.Checkbox('2v2_35.65_35.65_43',key='2v2_35.65_35.65_1.1_43', default=False),sg.Checkbox('2v2_35.65_35.65_53',key='2v2_35.65_35.65_1.1_53', default=False),sg.Checkbox('2v2_35.65_35.65_63',key='2v2_35.65_35.65_1.1_63', default=False),sg.Checkbox('2v2_35.65_35.65_73',key='2v2_35.65_35.65_1.1_73', default=False)],
[sg.Checkbox('2v2_35.65_65.35_43',key='2v2_35.65_65.35_1.1_43', default=False),sg.Checkbox('2v2_35.65_65.35_53',key='2v2_35.65_65.35_1.1_53', default=False),sg.Checkbox('2v2_35.65_65.35_63',key='2v2_35.65_65.35_1.1_63', default=False),sg.Checkbox('2v2_35.65_65.35_73',key='2v2_35.65_65.35_1.1_73', default=False)],
[sg.Checkbox('2v2_70.30_70.30_43',key='2v2_70.30_70.30_1.1_43', default=False),sg.Checkbox('2v2_70.30_70.30_53',key='2v2_70.30_70.30_1.1_53', default=False),sg.Checkbox('2v2_70.30_70.30_63',key='2v2_70.30_70.30_1.1_63', default=False),sg.Checkbox('2v2_70.30_70.30_73',key='2v2_70.30_70.30_1.1_73', default=False)],
[sg.Checkbox('2v2_70.30_50.50_43',key='2v2_70.30_50.50_1.1_43', default=False),sg.Checkbox('2v2_70.30_50.50_53',key='2v2_70.30_50.50_1.1_53', default=False),sg.Checkbox('2v2_70.30_50.50_63',key='2v2_70.30_50.50_1.1_63', default=False),sg.Checkbox('2v2_70.30_50.50_73',key='2v2_70.30_50.50_1.1_73', default=False)],
[sg.Text("Task"), sg.OptionMenu(values=('asterisk','single',"big_random","Multigoal","MultigoalFixed",
"Rotation_single", "Rotation_region","big_Rotation", "full_task","big_full_task", 'multi', "direction", "wall", "wall_single"), k='-task', default_value='unplanned_random')],
[sg.Text("Reward"), sg.OptionMenu(values=('Sparse','Distance','Distance + Finger', 'Hinge Distance + Finger', 'Slope', 'Slope + Finger','SmartDistance + Finger','SmartDistance + SmartFinger','ScaledDistance + Finger','ScaledDistance+ScaledFinger', 'SFS','DFS'), k='-reward',default_value='ScaledDistance+ScaledFinger')],
[sg.Checkbox("Object Start Position", key='-rstart',default=False), sg.Checkbox("Relative Finger Position", key='-rfinger',default=False),sg.Checkbox("Object Orientation", key='-ror',default=False), sg.Checkbox("Finger Open", key='-rfo',default=False)],
[sg.Text('Rotation limits, only used by Rotation and Full Tasks'), sg.Radio('75 degrees',group_id='rots',key='-75',default=False), sg.Radio('50 degrees',group_id='rots',key='-50',default=True), sg.Radio('15 degrees',group_id='rots',key='-15',default=False)],
[sg.Text('Replay Buffer Sampling'), sg.OptionMenu(values=['priority', 'random','random+expert'], k='-sampling', default_value='priority')],
[sg.Text('Domain Randomization Options')],
[sg.Checkbox('Finger Friction', default=True, k='-DRFI'),sg.Checkbox('Floor Friction', default=True, k='-DRFL'),sg.Checkbox('Object Size', default=True, k='-DROS'), sg.Checkbox('Object Mass', default=True, k='-DROM')]]
model_layout = [ [sg.Text('Num Epochs'), sg.Input(1000000, key='-epochs',size=(8, 2)), sg.Text('Batch Size'), sg.Input(100, key='-batch-size',size=(8, 2))],
[sg.Text('Learning Rate'), sg.Input(0.0001,key='-learning',size=(8, 2)), sg.Text('Discount Factor'), sg.Input(0.995, key='-df',size=(8, 2))],
[sg.Text('Starting Epsilon'), sg.Input(0.7,key='-epsilon',size=(8, 2)), sg.Text('Epsilon Decay Rate'), sg.Input(0.998, key='-edecay',size=(8, 2))],
[sg.Text('Rollout Size'), sg.Input(5,key='-rollout_size',size=(8, 2)), sg.Text('Rollout Weight'), sg.Input(0.5, key='-rollout_weight',size=(8, 2))],
[sg.Text('Evaluation Period'), sg.Input(10000,key='-eval',size=(8, 2)), sg.Text('Tau'), sg.Input(0.0005, key='-tau',size=(8, 2))],
[sg.Text('Timesteps per Episode'), sg.Input(25,key='-tsteps',size=(8, 2)), sg.Text('Timesteps in Evaluation'), sg.Input(25,key='-eval-tsteps',size=(8, 2))],
[sg.Text('State Training Noise'), sg.Input(0.0, key='-snoise',size=(8, 2)),sg.Text('Start Pos Range (mm)'), sg.Input(0, key='-start-noise',size=(8, 2))],
[sg.Text('Timestep Frequency'), sg.Input(3,key='-freq',size=(8, 2)), sg.Text('Entropy'), sg.Input(0.0,key='-entropy',size=(8, 2))],
[sg.Text('Finger off object frequency'), sg.Input(0.0, key='-fobfreq', size=(8,2))],
[sg.Checkbox('Fingers Start in Contact', default=False, key='-contact_start'),sg.Checkbox('Cirriculum',default=False,key='-cirriculum')]]
plotting_layout = [[sg.Text('Model Title')],
[sg.Input('test1',key='-title')],
[sg.Col([[sg.Text('Worker State')],[sg.Checkbox('Finger Tip Position', default=True, k='-ftp')],
[sg.Checkbox('Finger Base Position', default=False, k='-fbp')],
[sg.Checkbox('Finger Contact Position', default=False, k='-fcp')],
[sg.Checkbox('Joint Angle', default=False, k='-ja')],
[sg.Checkbox('Object Position', default=True, k='-op')],
[sg.Checkbox('Object Orientation', default=True, k='-oo')],
[sg.Checkbox('Object Angle', default=False,k='-oa')],
[sg.Checkbox('Finger Object Distance', default=False, k='-fod')],
[sg.Checkbox('Finger Tip Angle', default=False, k='-fta')],
[sg.Checkbox('Goal Position', default=True, k='-gp')],
[sg.Checkbox('Goals Achieved', default=False, k='-ga')],
[sg.Checkbox('Goal Orientation', default=True, k = '-go')],
[sg.Checkbox('Goal Finger Pos', default=False, k='-gf')],
[sg.Checkbox('Goal Finger Separation', default=False, k='-gfs')],
[sg.Checkbox('HandParameters', default=False,key='-params')],
[sg.Checkbox('Timesteps Remaining', default=False,key='-timesteps_state')],
[sg.Checkbox('Image Stamp', key='-ims')],
[sg.Checkbox('Image Fill', key='-imf')],
[sg.Checkbox('Image Obstacle', key='-imo')],
[sg.Checkbox('WallPose', default=False,key='-wall')]]),
sg.Col([[sg.Text('Manager State')],[sg.Checkbox('Finger Tip Position', default=True, k='-mftp')],
[sg.Checkbox('Finger Base Position', default=False, k='-mfbp')],
[sg.Checkbox('Finger Contact Position', default=False, k='-mfcp')],
[sg.Checkbox('Joint Angle', default=False, k='-mja')],
[sg.Checkbox('Object Position', default=True, k='-mop')],
[sg.Checkbox('Object Orientation', default=True, k='-moo')],
[sg.Checkbox('Object Angle', default=False,k='-moa')],
[sg.Checkbox('Finger Object Distance', default=False, k='-mfod')],
[sg.Checkbox('Finger Tip Angle', default=False, k='-mfta')],
[sg.Checkbox('Goal Position', default=True, k='-mgp')],
[sg.Checkbox('Goals Achieved', default=False, k='-mga')],
[sg.Checkbox('Goal Orientation', default=True, k = '-mgo')],
[sg.Checkbox('Goal Finger Pos', default=False, k='-mgf')],
[sg.Checkbox('Goal Finger Separation', default=False, k='-mgfs')],
[sg.Checkbox('HandParameters', default=False,key='-mparams')],
[sg.Checkbox('Timesteps Remaining', default=False,key='-mtimesteps_state')],
[sg.Checkbox('Image Stamp', key='-mims')],
[sg.Checkbox('Image Fill', key='-mimf')],
[sg.Checkbox('Image Obstacle', key='-mimo')],
[sg.Checkbox('WallPose', default=False,key='-mwall')]])],
[sg.Text('Num Previous States'), sg.Input(2, k='-pv',size=(8, 2)), sg.Text('Success Radius (mm)'), sg.Input(2, key='-sr',size=(8, 2))],
[sg.Text("Distance Scale"), sg.Input(1,key='-distance_scale',size=(8, 2)), sg.Text('Contact Scale'), sg.Input(0.2,key='-contact_scale',size=(8, 2)), sg.Text('Success Reward'), sg.Input(1,key='-success_reward',size=(8, 2)), sg.Text('Rotation Scale'), sg.Input(1,key='-rotation_scale',size=(8, 2))],
[sg.Text("Low Level Action"), sg.OptionMenu(values=('Joint Velocity','Finger Tip Position'), k='-action',default_value='Finger Tip Position')],
[sg.Text('Manager Action'), sg.OptionMenu(values=("Object Pose", "Object XY","Object+Finger"), k='-manager_action',default_value='Object Pose')],
[sg.Text('Number of Goals for Manager'), sg.Input(1, k='-manager_goals',size=(8, 2))],
[sg.Checkbox('Vizualize Simulation', default=False, k='-viz'), sg.Checkbox('IK every sim step?', default=False, key='-ik-freq')],
[sg.Button('Build Config File', key='-build')]]
layout = [[sg.TabGroup([[sg.Tab('Task and General parameters', data_layout, key='-mykey-'),
sg.Tab('Hyperparameters', model_layout),
sg.Tab('State, Action, Reward', plotting_layout)]], key='-group1-', tab_location='top', selected_title_color='purple')]]
self.data_type = None
self.window = sg.Window('RNN Gui', layout, return_keyboard_events=True, use_default_focus=False, finalize=True)
def build_args(self, values):
self.built = False
self.args = {'epochs': int(values['-epochs']),
'batch_size': int(values['-batch-size']),
'model': values['-model'],
'learning_rate': float(values['-learning']),
'discount': float(values['-df']),
'epsilon': float(values['-epsilon']),
'edecay': float(values['-edecay']),
'entropy': float(values['-entropy']),
'object': values['-object'],
'task': values['-task'],
'evaluate': int(values['-eval']),
'sampling': values['-sampling'],
'reward': values['-reward'],
'action': values['-action'],
'manager_action': values['-manager_action'],
'rollout_size': int(values['-rollout_size']),
'rollout_weight': float(values['-rollout_weight']),
'tau': float(values['-tau']),
'pv': int(values['-pv']),
'viz': int(values['-viz']),
'sr': int(values['-sr']),
'success_reward': float(values['-success_reward']),
'state_noise': float(values['-snoise']),
'start_noise': float(values['-start-noise']),
'tsteps': int(values['-tsteps']),
'eval-tsteps':int(values['-eval-tsteps']),
'distance_scaling': float(values['-distance_scale']),
'contact_scaling': float(values['-contact_scale']),
'rotation_scaling': float(values['-rotation_scale']),
'freq': int(values['-freq']),
'IK_freq': bool(values['-ik-freq']),
'fobfreq': float(values['-fobfreq']),
'object_random_start': bool(values['-rstart']),
'finger_random_start': bool(values['-rfinger']),
'object_random_orientation': bool(values['-ror']),
'finger_random_off': bool(values['-rfo']),
'domain_randomization_finger_friction':bool(values['-DRFI']),
'domain_randomization_floor_friction':bool(values['-DRFL']),
'domain_randomization_object_size':bool(values['-DROS']),
'domain_randomization_object_mass':bool(values['-DROM']),
'contact_start':bool(values['-contact_start']),
'cirriculum':bool(values['-cirriculum']),
'manager goals':int(values['-manager_goals'])}
state_len = 0
state_mins = []
state_maxes = []
state_list = []
manager_state_mins=[]
manager_state_maxes =[]
manager_state_list = []
manager_state_len = 0
if values['-ftp']:
state_mins.extend([-0.072, 0.018, -0.072, 0.018])
state_maxes.extend([0.072, 0.172, 0.072, 0.172])
state_len += 4
state_list.append('ftp')
if values['-fbp']:
state_mins.extend([-0.072, 0.018, -0.072, 0.018])
state_maxes.extend([0.072, 0.172, 0.072, 0.172])
state_len += 4
state_list.append('fbp')
if values['-fcp']:
state_mins.extend([-0.072, 0.018, -0.072, 0.018])
state_maxes.extend([0.072, 0.172, 0.072, 0.172])
state_len += 4
state_list.append('fcp')
if values['-op']:
state_mins.extend([-0.072, 0.018])
state_maxes.extend([0.072, 0.172])
state_len += 2
state_list.append('op')
if values['-oo']:
state_mins.extend([-1,-1,-1,-1])
state_maxes.extend([1,1,1,1])
state_len += 4
state_list.append('oo')
if values['-oa']:
state_mins.extend([-1,-1])
state_maxes.extend([1,1])
state_len += 2
state_list.append('oa')
if values['-ja']:
state_mins.extend([-np.pi/2, -2.09, -np.pi/2, 0])
state_maxes.extend([np.pi/2, 0, np.pi/2, 2.09])
state_len += 4
state_list.append('ja')
if values['-fod']:
state_mins.extend([-0.001, -0.001])
state_maxes.extend([0.072, 0.072])
state_len += 2
state_list.append('fod')
if values['-fta']:
state_mins.extend([-np.pi/2-2.09, -np.pi/2])
state_maxes.extend([np.pi/2, np.pi/2+2.09])
state_len += 2
state_list.append('fta')
if values['-params']:
state_mins.extend([0.0504,0.0432,0.0504,0.0432,0.053])
state_maxes.extend([0.1008,0.0936,0.1008,0.0936,0.073])
state_len += 5
state_list.append('params')
if values['-timesteps_state']:
state_mins.append(0)
state_maxes.append(25)
state_len += 1
state_list.append('tstep')
if values['-gp']:
state_mins.extend([-0.08, -0.08])
state_maxes.extend([0.08, 0.08])
state_len += 2
state_list.append('lgp')
if values['-ga']:
state_mins.extend([0])
state_maxes.extend([1])
state_len += 1
state_list.append('lga')
if values['-go']:
if values['-75']:
state_mins.append(-75/180*np.pi)
state_maxes.append(75/180*np.pi)
elif values['-50']:
state_mins.append(-50/180*np.pi)
state_maxes.append(50/180*np.pi)
elif values['-15']:
state_mins.append(-15/180*np.pi)
state_maxes.append(15/180*np.pi)
state_len += 1
state_list.append('lgo')
if values['-gf']:
state_mins.extend([-0.072, 0.018, -0.072, 0.018])
state_maxes.extend([0.072, 0.172, 0.072, 0.172])
state_len += 4
state_list.append('lgf')
if values ['-gfs']:
state_mins.extend([-0.04, -0.04])
state_maxes.extend([0.04, 0.04])
state_len += 2
state_list.append('lgfs')
if values['-wall']:
state_mins.extend([-0.08,0.02,-1,-1,-1,-1])
state_maxes.extend([0.08,0.18,1,1,1,1])
state_len += 6
state_list.append('wall')
if self.args['pv'] > 0:
state_len += state_len * self.args['pv']
temp_mins = state_mins.copy()
temp_maxes = state_maxes.copy()
for i in range(self.args['pv']):
state_mins.extend(temp_mins)
state_maxes.extend(temp_maxes)
"""
manager state space shenanigans"""
if values['-mftp']:
manager_state_mins.extend([-0.072, 0.018, -0.072, 0.018])
manager_state_maxes.extend([0.072, 0.172, 0.072, 0.172])
manager_state_len += 4
manager_state_list.append('ftp')
if values['-mfbp']:
manager_state_mins.extend([-0.072, 0.018, -0.072, 0.018])
manager_state_maxes.extend([0.072, 0.172, 0.072, 0.172])
manager_state_len += 4
manager_state_list.append('fbp')
if values['-mfcp']:
manager_state_mins.extend([-0.072, 0.018, -0.072, 0.018])
manager_state_maxes.extend([0.072, 0.172, 0.072, 0.172])
manager_state_len += 4
manager_state_list.append('fcp')
if values['-mop']:
manager_state_mins.extend([-0.072, 0.018])
manager_state_maxes.extend([0.072, 0.172])
manager_state_len += 2
manager_state_list.append('op')
if values['-moo']:
manager_state_mins.extend([-1,-1,-1,-1])
manager_state_maxes.extend([1,1,1,1])
manager_state_len += 4
manager_state_list.append('oo')
if values['-moa']:
manager_state_mins.extend([-1,-1])
manager_state_maxes.extend([1,1])
manager_state_len += 2
manager_state_list.append('oa')
if values['-mja']:
manager_state_mins.extend([-np.pi/2, -2.09, -np.pi/2, 0])
manager_state_maxes.extend([np.pi/2, 0, np.pi/2, 2.09])
manager_state_len += 4
manager_state_list.append('ja')
if values['-mfod']:
manager_state_mins.extend([-0.001, -0.001])
manager_state_maxes.extend([0.072, 0.072])
manager_state_len += 2
manager_state_list.append('fod')
if values['-mfta']:
manager_state_mins.extend([-np.pi/2-2.09, -np.pi/2])
manager_state_maxes.extend([np.pi/2, np.pi/2+2.09])
manager_state_len += 2
manager_state_list.append('fta')
if values['-mparams']:
manager_state_mins.extend([0.0504,0.0432,0.0504,0.0432,0.053])
manager_state_maxes.extend([0.1008,0.0936,0.1008,0.0936,0.073])
manager_state_len += 5
state_list.append('params')
if values['-mtimesteps_state']:
manager_state_mins.append(0)
manager_state_maxes.append(25)
manager_state_len += 1
manager_state_list.append('tstep')
if values['-mgp']:
manager_state_mins.extend([-0.07, -0.07]*int(values['-manager_goals']))
manager_state_maxes.extend([0.07, 0.07]*int(values['-manager_goals']))
manager_state_len += 2*int(values['-manager_goals'])
manager_state_list.append('gp')
if values['-mgp']:
manager_state_mins.extend([0]*int(values['-manager_goals']))
manager_state_maxes.extend([1]*int(values['-manager_goals']))
manager_state_len += int(values['-manager_goals'])
manager_state_list.append('ga')
if values['-mgo']:
if values['-75']:
manager_state_mins.append(-75/180*np.pi)
manager_state_maxes.append(75/180*np.pi)
elif values['-50']:
manager_state_mins.append(-50/180*np.pi)
manager_state_maxes.append(50/180*np.pi)
elif values['-15']:
manager_state_mins.append(-15/180*np.pi)
manager_state_maxes.append(15/180*np.pi)
manager_state_len += 1
manager_state_list.append('go')
if values['-mgf']:
manager_state_mins.extend([-0.072, 0.018, -0.072, 0.018])
manager_state_maxes.extend([0.072, 0.172, 0.072, 0.172])
manager_state_len += 4
manager_state_list.append('gf')
if values ['-mgfs']:
manager_state_mins.extend([-0.04, -0.04])
manager_state_maxes.extend([0.04, 0.04])
manager_state_len += 2
manager_state_list.append('gfs')
if values['-mims']:
manager_state_mins.extend([0]*240*240)
manager_state_maxes.extend([255]*240*240)
manager_state_len += 240*240
manager_state_list.append('mims')
if values['-mimf']:
manager_state_mins.extend([0]*240*240)
manager_state_maxes.extend([255]*240*240)
manager_state_len += 240*240
manager_state_list.append('mimf')
if values['-mimo']:
manager_state_mins.extend([0]*240*240)
manager_state_maxes.extend([255]*240*240)
manager_state_len += 240*240
manager_state_list.append('mimo')
if values['-mwall']:
manager_state_mins.extend([-0.08,0.02,-1,-1,-1,-1])
manager_state_maxes.extend([0.08,0.18,1,1,1,1])
manager_state_len += 6
manager_state_list.append('wall')
if self.args['pv'] > 0:
manager_state_len += manager_state_len * self.args['pv']
temp_mins = manager_state_mins.copy()
temp_maxes = manager_state_maxes.copy()
for i in range(self.args['pv']):
manager_state_mins.extend(temp_mins)
manager_state_maxes.extend(temp_maxes)
if state_len == 0:
print('No selected state space')
return False
if (self.args['task'] == 'asterisk') or (self.args['task'] == 'random'):
if not values['-gp']:
print('Goal position needed for multigoal tasks')
return False
self.args['worker_state_dim'] = state_len
self.args['worker_state_mins'] = state_mins
self.args['worker_state_maxes'] = state_maxes
self.args['worker_state_list'] = state_list
self.args['manager_state_dim'] = manager_state_len
self.args['manager_state_mins'] = manager_state_mins
self.args['manager_state_maxes'] = manager_state_maxes
self.args['manager_state_list'] = manager_state_list
if self.args['action'] =='Joint Velocity' or self.args['action'] =='Finger Tip Position':
self.args['action_dim'] = 4
elif self.args['action'] == 'Object Pose':
self.args['action_dim'] = 3
if self.args['manager_action'] == 'Object Pose':
self.args['manager_action_dim'] = 3
self.args['manager_maxes'] = [0.08, 0.08, 50/180*np.pi]
self.args['manager_mins'] = [-0.08,-0.08,-50/180*np.pi]
elif self.args['manager_action'] == 'Object XY':
self.args['manager_action_dim'] = 2
self.args['manager_maxes'] = [0.08, 0.08]
self.args['manager_mins'] = [-0.08,-0.08]
elif self.args['manager_action'] == 'Object+Finger':
self.args['manager_action_dim'] = 5
self.args['manager_maxes'] = [0.08, 0.08, 50/180*np.pi, 0.10, 0.04]
self.args['manager_mins'] = [-0.08,-0.08,-50/180*np.pi, 0.02, -0.04]
if 'FD' in self.args['model']:
exists = os.path.isfile(self.expert_path + 'episode_all.pkl')
if not exists:
print('Selected FD model but no expert data loaded')
return False
else:
self.args['edata'] = values['-browse-expert'] + 'episode_all.pkl'
if os.path.isdir(self.save_path) and self.save_path != '/':
self.args['save_path'] = self.save_path + '/'
self.args['load_path'] = self.load_path + '/'
else:
print('save path is not a valid directory')
return False
overall_path = pathlib.Path(__file__).parent.resolve()
resource_path = overall_path.joinpath('demos/rl_demo/resources')
run_path = overall_path.joinpath('demos/rl_demo/runs')
self.args['hand_path'] = str(resource_path.joinpath("hand_bank"))
self.args['hand_file_list'] = []
for k,v in values.items():
if type(k) == str:
if '2v2' in k:
if v:
print('adding thing', k+'/hand/'+k+'.urdf')
self.args['hand_file_list'].append(k+'/hand/'+k+'.urdf')
if values['-object'] == 'Cube':
if self.args['domain_randomization_object_size']:
self.args['object_path'] = [str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cuboid_small.urdf')),
str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cuboid_small_sub10.urdf')),
str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cuboid_small_add10.urdf'))]
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cuboid_small.urdf'))]
elif values['-object'] == 'Cylinder':
if self.args['domain_randomization_object_size']:
self.args['object_path'] = [str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cylinder_small_alt.urdf')),
str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cylinder_small_alt_sub10.urdf')),
str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cylinder_small_alt_add10.urdf'))]
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/2v2_mod/2v2_mod_cylinder_small_alt.urdf'))]
elif values['-object'] == 'circle':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/20_r_circle.urdf'))]
elif values['-object'] == 'hourglass':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/20_r_hourglass.urdf'))]
elif values['-object'] == 'ellipse':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/20x12p5_ellipse.urdf'))]
elif values['-object'] == 'square_concave':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/40_square_40_concave.urdf'))]
elif values['-object'] == 'square':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/40x40_square.urdf'))]
elif values['-object'] == 'triangle':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/40x40_triangle.urdf'))]
elif values['-object'] == 'cone':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/45_10_slope_cone.urdf'))]
elif values['-object'] == 'teardrop':
if self.args['domain_randomization_object_size']:
raise NotImplementedError('Sphere size randomization not implemented')
else:
self.args['object_path'] = [str(resource_path.joinpath('object_models/Jeremiah_Shapes/50x30_teardrop.urdf'))]
if values['-action'] == 'Joint Velocity':
self.args['max_action'] = 1.57
elif values['-action'] == 'Finger Tip Position':
self.args['max_action'] = 0.01
elif values['-action'] == 'Object Pose':
self.args['max_action'] = [0.01,0.01,1.57]
if (values['-task'] == 'full_random') | (values['-task'] == 'unplanned_random'):
self.args['points_path'] = str(resource_path.joinpath('points.csv'))
self.args['test_path'] = str(resource_path.joinpath('test_points.csv'))
elif (values['-task'] == 'big_random') | (values['-task'] =='multi')|('Multigoal' in values['-task']):
self.args['points_path'] = str(resource_path.joinpath('train_points_big.csv'))
self.args['test_path'] = str(resource_path.joinpath('test_points_big.csv'))
elif (values['-task'] =='Rotation_region')|(values['-task'] =='full_task'):
if values['-75']:
self.args['points_path'] = str(resource_path.joinpath('rotation_only_train_75.csv'))
self.args['test_path'] = str(resource_path.joinpath('rotation_only_test_75.csv'))
if values['-50']:
self.args['points_path'] = str(resource_path.joinpath('rotation_only_train.csv'))
self.args['test_path'] = str(resource_path.joinpath('rotation_only_test.csv'))
elif values['-15']:
self.args['points_path'] = str(resource_path.joinpath('rotation_only_train_15.csv'))
self.args['test_path'] = str(resource_path.joinpath('rotation_only_test_15.csv'))
elif values['-task'] == 'Rotation_single':
if values['-75']:
self.args['points_path'] = str(resource_path.joinpath('solo_rotation_75.csv'))
self.args['test_path'] = str(resource_path.joinpath('solo_rotation_75.csv'))
if values['-50']:
self.args['points_path'] = str(resource_path.joinpath('solo_rotation_50.csv'))
self.args['test_path'] = str(resource_path.joinpath('solo_rotation_50.csv'))
elif values['-15']:
self.args['points_path'] = str(resource_path.joinpath('solo_rotation_15.csv'))
self.args['test_path'] = str(resource_path.joinpath('solo_rotation_15.csv'))
elif (values['-task'] =='big_Rotation') | (values['-task'] =='big_full_task'):
if values['-50']:
self.args['points_path'] = str(resource_path.joinpath('Big_rotation_50_train.csv'))
self.args['test_path'] = str(resource_path.joinpath('Big_rotation_50_test.csv'))
elif values['-15']:
self.args['points_path'] = str(resource_path.joinpath('Big_rotation_15_train.csv'))
self.args['test_path'] = str(resource_path.joinpath('Big_rotation_15_test.csv'))
elif values['-task'] =='wall':
self.args['points_path'] = str(resource_path.joinpath('train_wall_poses.csv'))
self.args['test_path'] = str(resource_path.joinpath('test_wall_poses.csv'))
elif values['-task'] =='wall_single':
self.args['points_path'] = str(resource_path.joinpath('single_wall.csv'))
self.args['test_path'] = str(resource_path.joinpath('single_wall.csv'))
else:
self.args['points_path'] = ''
if self.args['task'] == 'single':
for name in self.single_names:
os.mkdir(self.save_path + '/'+name+'/')
self.args['save_path'] = self.save_path + '/' + name + '/'
self.args['tname'] = str(run_path.joinpath(values['-title']).joinpath(name))
self.args['task'] = name
self.built = True
self.log_params()
elif self.args['task'] == 'wedge':
print('aight')
for name in self.single_names:
os.mkdir(self.save_path + '/wedge_'+name+'/')
self.args['save_path'] = self.save_path + '/wedge_' + name + '/'
self.args['tname'] = str(run_path.joinpath(values['-title']).joinpath("wedge_"+name))
self.args['task'] = 'wedge_' + name
self.args['points_path'] = str(resource_path.joinpath('wedge_'+name+'.csv'))
self.built = True
self.log_params()
elif self.args['task'] == 'double_wedge':
print('aight 2')
for name in self.double_names:
os.mkdir(self.save_path + '/wedge_'+name+'/')
self.args['save_path'] = self.save_path + '/wedge_' + name + '/'
self.args['tname'] = str(run_path.joinpath(values['-title']).joinpath("wedge_"+name))
self.args['task'] = 'wedge_' + name
self.args['points_path'] = str(resource_path.joinpath('wedge_'+name+'.csv'))
self.built = True
self.log_params()
elif self.args['task'] == 'clump_wedge':
for name in self.alt_double_names:
os.mkdir(self.save_path + '/wedge_'+name+'/')
self.args['save_path'] = self.save_path + '/wedge_' + name + '/'
self.args['tname'] = str(run_path.joinpath(values['-title']).joinpath("wedge_"+name))
self.args['task'] = 'wedge_' + name
self.args['points_path'] = str(resource_path.joinpath('wedge_'+name+'.csv'))
self.built = True
self.log_params()
else:
self.args['tname'] = str(run_path.joinpath(values['-title']))
self.built = True
self.log_params()
return True
def log_params(self):
if self.built:
print('saving configuration')
with open(self.args['save_path'] + '/experiment_config.json', 'w') as conf_file:
json.dump(self.args, conf_file, indent=4)
try:
os.mkdir(self.args['save_path'] + '/Train/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Test/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Videos/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Plots/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Eval_A/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Real_B/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Real_A/')
except FileExistsError:
pass
try:
os.mkdir(self.args['save_path'] + '/Eval_B/')
except FileExistsError:
pass
else:
print('config not built, parameters not saved')
def run_gui(self):
p1 = pathlib.Path(__file__).parent.resolve()
values = {'-task':'unplanned_random'}
while True:
prev = values['-task']
event, values = self.window.Read()
# print(values.keys())
print('event happened')
# --------------------- Button & Keyboard ---------------------
if event == sg.WIN_CLOSED:
break
elif event == 'shuffle-type':
self.shuffle_type = values['shuffle-type']
elif event == '-load-model':
newfolder = sg.popup_get_file('Select Model File', no_window=True)
if newfolder is None:
continue
if newfolder.lower().endswith('.pt'):
self.model_path = newfolder
elif event == 'Exit':
break
# ----------------- Menu choices -----------------
if event == '-browse-expert':
newfolder = sg.popup_get_folder('Select Folder Containing Expert Data',initial_folder=str(p1)+'/demos/rl_demo/data', no_window=True)
if newfolder is None:
continue
folder = newfolder
print(type(folder))
self.expert_path = folder
self.window.refresh()
elif event == '-browse-save':
newfolder = sg.popup_get_folder('Select Folder To Save Data In',initial_folder=str(p1)+'/demos/rl_demo/data', no_window=True)
if newfolder is None:
continue
folder = newfolder
print(type(folder))
self.save_path = folder
self.window.refresh()
elif event == '-browse-load':
newfolder = sg.popup_get_folder('Select Folder To Save Data In',initial_folder=str(p1)+'/demos/rl_demo/data', no_window=True)
if newfolder is None:
continue
folder = newfolder
print(type(folder))
self.load_path = folder
self.window.refresh()
elif event == '-build':
ready = self.build_args(values)
if ready:
print('Build Successful')
else:
print('Build Not Successful')
if values['-task'] !=prev:
if 'contact' in values['-task']:
self.window.Element("-reward").Update(values=RNNGui.finger_rewards)
elif 'Rotation' in values['-task']:
self.window.Element("-reward").Update(values=RNNGui.rotate_rewards)
elif values['-task'] =='full_task':
self.window.Element("-reward").Update(values=RNNGui.full_task_rewards)
elif 'wall' in values['-task']:
self.window.Element('-reward').Update(values=RNNGui.wall_task_rewards)
elif 'Multi' in values['-task']:
self.window.Element('-reward').Update(values=RNNGui.multi_rewards)
else:
self.window.Element("-reward").Update(values=RNNGui.slide_rewards)
# elif event == '-update':
# print(values['-update'])
elif event == 'About':
sg.popup('Why you click me?',
'Go harrass Nigel with questions. [email protected]')
self.window['-save-path'].update(self.save_path)
self.window['-expert-path'].update(self.expert_path)
self.window['-load-path'].update(self.load_path)
self.window.close()
def main():
backend = RNNGui()
backend.run_gui()
if __name__ == '__main__':
main()