-
Notifications
You must be signed in to change notification settings - Fork 61
/
environment.py
281 lines (201 loc) · 12.5 KB
/
environment.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
from tkinter import *
from tkinter import ttk
import time
import numpy as np
from mujoco_py import load_model_from_path, MjSim, MjViewer
class Environment():
def __init__(self, model_name, goal_space_train, goal_space_test, project_state_to_end_goal, end_goal_thresholds, initial_state_space, subgoal_bounds, project_state_to_subgoal, subgoal_thresholds, max_actions = 1200, num_frames_skip = 10, show = False):
self.name = model_name
# Create Mujoco Simulation
self.model = load_model_from_path("./mujoco_files/" + model_name)
self.sim = MjSim(self.model)
# Set dimensions and ranges of states, actions, and goals in order to configure actor/critic networks
if model_name == "pendulum.xml":
self.state_dim = 2*len(self.sim.data.qpos) + len(self.sim.data.qvel)
else:
self.state_dim = len(self.sim.data.qpos) + len(self.sim.data.qvel) # State will include (i) joint angles and (ii) joint velocities
self.action_dim = len(self.sim.model.actuator_ctrlrange) # low-level action dim
self.action_bounds = self.sim.model.actuator_ctrlrange[:,1] # low-level action bounds
self.action_offset = np.zeros((len(self.action_bounds))) # Assumes symmetric low-level action ranges
self.end_goal_dim = len(goal_space_test)
self.subgoal_dim = len(subgoal_bounds)
self.subgoal_bounds = subgoal_bounds
# Projection functions
self.project_state_to_end_goal = project_state_to_end_goal
self.project_state_to_subgoal = project_state_to_subgoal
# Convert subgoal bounds to symmetric bounds and offset. Need these to properly configure subgoal actor networks
self.subgoal_bounds_symmetric = np.zeros((len(self.subgoal_bounds)))
self.subgoal_bounds_offset = np.zeros((len(self.subgoal_bounds)))
for i in range(len(self.subgoal_bounds)):
self.subgoal_bounds_symmetric[i] = (self.subgoal_bounds[i][1] - self.subgoal_bounds[i][0])/2
self.subgoal_bounds_offset[i] = self.subgoal_bounds[i][1] - self.subgoal_bounds_symmetric[i]
# End goal/subgoal thresholds
self.end_goal_thresholds = end_goal_thresholds
self.subgoal_thresholds = subgoal_thresholds
# Set inital state and goal state spaces
self.initial_state_space = initial_state_space
self.goal_space_train = goal_space_train
self.goal_space_test = goal_space_test
self.subgoal_colors = ["Magenta","Green","Red","Blue","Cyan","Orange","Maroon","Gray","White","Black"]
self.max_actions = max_actions
# Implement visualization if necessary
self.visualize = show # Visualization boolean
if self.visualize:
self.viewer = MjViewer(self.sim)
self.num_frames_skip = num_frames_skip
# Get state, which concatenates joint positions and velocities
def get_state(self):
if self.name == "pendulum.xml":
return np.concatenate([np.cos(self.sim.data.qpos),np.sin(self.sim.data.qpos),
self.sim.data.qvel])
else:
return np.concatenate((self.sim.data.qpos, self.sim.data.qvel))
# Reset simulation to state within initial state specified by user
def reset_sim(self):
# Reset joint positions and velocities
for i in range(len(self.sim.data.qpos)):
self.sim.data.qpos[i] = np.random.uniform(self.initial_state_space[i][0],self.initial_state_space[i][1])
for i in range(len(self.sim.data.qvel)):
self.sim.data.qvel[i] = np.random.uniform(self.initial_state_space[len(self.sim.data.qpos) + i][0],self.initial_state_space[len(self.sim.data.qpos) + i][1])
self.sim.step()
# Return state
return self.get_state()
# Execute low-level action for number of frames specified by num_frames_skip
def execute_action(self, action):
self.sim.data.ctrl[:] = action
for _ in range(self.num_frames_skip):
self.sim.step()
if self.visualize:
self.viewer.render()
return self.get_state()
# Visualize end goal. This function may need to be adjusted for new environments.
def display_end_goal(self,end_goal):
# Goal can be visualized by changing the location of the relevant site object.
if self.name == "pendulum.xml":
self.sim.data.mocap_pos[0] = np.array([0.5*np.sin(end_goal[0]),0,0.5*np.cos(end_goal[0])+0.6])
elif self.name == "ur5.xml":
theta_1 = end_goal[0]
theta_2 = end_goal[1]
theta_3 = end_goal[2]
# shoulder_pos_1 = np.array([0,0,0,1])
upper_arm_pos_2 = np.array([0,0.13585,0,1])
forearm_pos_3 = np.array([0.425,0,0,1])
wrist_1_pos_4 = np.array([0.39225,-0.1197,0,1])
# Transformation matrix from shoulder to base reference frame
T_1_0 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0.089159],[0,0,0,1]])
# Transformation matrix from upper arm to shoulder reference frame
T_2_1 = np.array([[np.cos(theta_1), -np.sin(theta_1), 0, 0],[np.sin(theta_1), np.cos(theta_1), 0, 0],[0,0,1,0],[0,0,0,1]])
# Transformation matrix from forearm to upper arm reference frame
T_3_2 = np.array([[np.cos(theta_2),0,np.sin(theta_2),0],[0,1,0,0.13585],[-np.sin(theta_2),0,np.cos(theta_2),0],[0,0,0,1]])
# Transformation matrix from wrist 1 to forearm reference frame
T_4_3 = np.array([[np.cos(theta_3),0,np.sin(theta_3),0.425],[0,1,0,0],[-np.sin(theta_3),0,np.cos(theta_3),0],[0,0,0,1]])
# Determine joint position relative to original reference frame
# shoulder_pos = T_1_0.dot(shoulder_pos_1)
upper_arm_pos = T_1_0.dot(T_2_1).dot(upper_arm_pos_2)[:3]
forearm_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(forearm_pos_3)[:3]
wrist_1_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(T_4_3).dot(wrist_1_pos_4)[:3]
joint_pos = [upper_arm_pos, forearm_pos, wrist_1_pos]
"""
print("\nEnd Goal Joint Pos: ")
print("Upper Arm Pos: ", joint_pos[0])
print("Forearm Pos: ", joint_pos[1])
print("Wrist Pos: ", joint_pos[2])
"""
for i in range(3):
self.sim.data.mocap_pos[i] = joint_pos[i]
else:
assert False, "Provide display end goal function in environment.py file"
# Function returns an end goal
def get_next_goal(self,test):
end_goal = np.zeros((len(self.goal_space_test)))
if self.name == "ur5.xml":
goal_possible = False
while not goal_possible:
end_goal = np.zeros(shape=(self.end_goal_dim,))
end_goal[0] = np.random.uniform(self.goal_space_test[0][0],self.goal_space_test[0][1])
end_goal[1] = np.random.uniform(self.goal_space_test[1][0],self.goal_space_test[1][1])
end_goal[2] = np.random.uniform(self.goal_space_test[2][0],self.goal_space_test[2][1])
# Next need to ensure chosen joint angles result in achievable task (i.e., desired end effector position is above ground)
theta_1 = end_goal[0]
theta_2 = end_goal[1]
theta_3 = end_goal[2]
# shoulder_pos_1 = np.array([0,0,0,1])
upper_arm_pos_2 = np.array([0,0.13585,0,1])
forearm_pos_3 = np.array([0.425,0,0,1])
wrist_1_pos_4 = np.array([0.39225,-0.1197,0,1])
# Transformation matrix from shoulder to base reference frame
T_1_0 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0.089159],[0,0,0,1]])
# Transformation matrix from upper arm to shoulder reference frame
T_2_1 = np.array([[np.cos(theta_1), -np.sin(theta_1), 0, 0],[np.sin(theta_1), np.cos(theta_1), 0, 0],[0,0,1,0],[0,0,0,1]])
# Transformation matrix from forearm to upper arm reference frame
T_3_2 = np.array([[np.cos(theta_2),0,np.sin(theta_2),0],[0,1,0,0.13585],[-np.sin(theta_2),0,np.cos(theta_2),0],[0,0,0,1]])
# Transformation matrix from wrist 1 to forearm reference frame
T_4_3 = np.array([[np.cos(theta_3),0,np.sin(theta_3),0.425],[0,1,0,0],[-np.sin(theta_3),0,np.cos(theta_3),0],[0,0,0,1]])
forearm_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(forearm_pos_3)[:3]
wrist_1_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(T_4_3).dot(wrist_1_pos_4)[:3]
# Make sure wrist 1 pos is above ground so can actually be reached
if np.absolute(end_goal[0]) > np.pi/4 and forearm_pos[2] > 0.05 and wrist_1_pos[2] > 0.15:
goal_possible = True
elif not test and self.goal_space_train is not None:
for i in range(len(self.goal_space_train)):
end_goal[i] = np.random.uniform(self.goal_space_train[i][0],self.goal_space_train[i][1])
else:
assert self.goal_space_test is not None, "Need goal space for testing. Set goal_space_test variable in \"design_env.py\" file"
for i in range(len(self.goal_space_test)):
end_goal[i] = np.random.uniform(self.goal_space_test[i][0],self.goal_space_test[i][1])
# Visualize End Goal
self.display_end_goal(end_goal)
return end_goal
# Visualize all subgoals
def display_subgoals(self,subgoals):
# Display up to 10 subgoals and end goal
if len(subgoals) <= 11:
subgoal_ind = 0
else:
subgoal_ind = len(subgoals) - 11
for i in range(1,min(len(subgoals),11)):
if self.name == "pendulum.xml":
self.sim.data.mocap_pos[i] = np.array([0.5*np.sin(subgoals[subgoal_ind][0]),0,0.5*np.cos(subgoals[subgoal_ind][0])+0.6])
# Visualize subgoal
self.sim.model.site_rgba[i][3] = 1
subgoal_ind += 1
elif self.name == "ur5.xml":
theta_1 = subgoals[subgoal_ind][0]
theta_2 = subgoals[subgoal_ind][1]
theta_3 = subgoals[subgoal_ind][2]
# shoulder_pos_1 = np.array([0,0,0,1])
upper_arm_pos_2 = np.array([0,0.13585,0,1])
forearm_pos_3 = np.array([0.425,0,0,1])
wrist_1_pos_4 = np.array([0.39225,-0.1197,0,1])
# Transformation matrix from shoulder to base reference frame
T_1_0 = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0.089159],[0,0,0,1]])
# Transformation matrix from upper arm to shoulder reference frame
T_2_1 = np.array([[np.cos(theta_1), -np.sin(theta_1), 0, 0],[np.sin(theta_1), np.cos(theta_1), 0, 0],[0,0,1,0],[0,0,0,1]])
# Transformation matrix from forearm to upper arm reference frame
T_3_2 = np.array([[np.cos(theta_2),0,np.sin(theta_2),0],[0,1,0,0.13585],[-np.sin(theta_2),0,np.cos(theta_2),0],[0,0,0,1]])
# Transformation matrix from wrist 1 to forearm reference frame
T_4_3 = np.array([[np.cos(theta_3),0,np.sin(theta_3),0.425],[0,1,0,0],[-np.sin(theta_3),0,np.cos(theta_3),0],[0,0,0,1]])
# Determine joint position relative to original reference frame
# shoulder_pos = T_1_0.dot(shoulder_pos_1)
upper_arm_pos = T_1_0.dot(T_2_1).dot(upper_arm_pos_2)[:3]
forearm_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(forearm_pos_3)[:3]
wrist_1_pos = T_1_0.dot(T_2_1).dot(T_3_2).dot(T_4_3).dot(wrist_1_pos_4)[:3]
joint_pos = [upper_arm_pos, forearm_pos, wrist_1_pos]
"""
print("\nSubgoal %d Joint Pos: " % i)
print("Upper Arm Pos: ", joint_pos[0])
print("Forearm Pos: ", joint_pos[1])
print("Wrist Pos: ", joint_pos[2])
"""
# Designate site position for upper arm, forearm and wrist
for j in range(3):
self.sim.data.mocap_pos[3 + 3*(i-1) + j] = np.copy(joint_pos[j])
self.sim.model.site_rgba[3 + 3*(i-1) + j][3] = 1
# print("\nLayer %d Predicted Pos: " % i, wrist_1_pos[:3])
subgoal_ind += 1
else:
# Visualize desired gripper position, which is elements 18-21 in subgoal vector
self.sim.data.mocap_pos[i] = subgoals[subgoal_ind]
# Visualize subgoal
self.sim.model.site_rgba[i][3] = 1
subgoal_ind += 1