forked from tonyzhaozh/act
-
Notifications
You must be signed in to change notification settings - Fork 0
/
scripted_policy.py
194 lines (152 loc) · 9.3 KB
/
scripted_policy.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
import numpy as np
import matplotlib.pyplot as plt
from pyquaternion import Quaternion
from detr.experiment_configs.sim_constants import SIM_TASK_CONFIGS
from ee_sim_env import make_ee_sim_env
import IPython
e = IPython.embed
class BasePolicy:
def __init__(self, inject_noise=False):
self.inject_noise = inject_noise
self.step_count = 0
self.left_trajectory = None
self.right_trajectory = None
def generate_trajectory(self, ts_first):
raise NotImplementedError
@staticmethod
def interpolate(curr_waypoint, next_waypoint, t):
t_frac = (t - curr_waypoint["t"]) / (next_waypoint["t"] - curr_waypoint["t"])
curr_xyz = curr_waypoint['xyz']
curr_quat = curr_waypoint['quat']
curr_grip = curr_waypoint['gripper']
next_xyz = next_waypoint['xyz']
next_quat = next_waypoint['quat']
next_grip = next_waypoint['gripper']
xyz = curr_xyz + (next_xyz - curr_xyz) * t_frac
quat = curr_quat + (next_quat - curr_quat) * t_frac
gripper = curr_grip + (next_grip - curr_grip) * t_frac
return xyz, quat, gripper
def __call__(self, ts):
# generate trajectory at first timestep, then open-loop execution
if self.step_count == 0:
self.generate_trajectory(ts)
# obtain left and right waypoints
if self.left_trajectory[0]['t'] == self.step_count:
self.curr_left_waypoint = self.left_trajectory.pop(0)
next_left_waypoint = self.left_trajectory[0]
if self.right_trajectory[0]['t'] == self.step_count:
self.curr_right_waypoint = self.right_trajectory.pop(0)
next_right_waypoint = self.right_trajectory[0]
# interpolate between waypoints to obtain current pose and gripper command
left_xyz, left_quat, left_gripper = self.interpolate(self.curr_left_waypoint, next_left_waypoint, self.step_count)
right_xyz, right_quat, right_gripper = self.interpolate(self.curr_right_waypoint, next_right_waypoint, self.step_count)
# Inject noise
if self.inject_noise:
scale = 0.01
left_xyz = left_xyz + np.random.uniform(-scale, scale, left_xyz.shape)
right_xyz = right_xyz + np.random.uniform(-scale, scale, right_xyz.shape)
action_left = np.concatenate([left_xyz, left_quat, [left_gripper]])
action_right = np.concatenate([right_xyz, right_quat, [right_gripper]])
self.step_count += 1
return np.concatenate([action_left, action_right])
class PickAndTransferPolicy(BasePolicy):
def generate_trajectory(self, ts_first):
init_mocap_pose_right = ts_first.observation['mocap_pose_right']
init_mocap_pose_left = ts_first.observation['mocap_pose_left']
box_info = np.array(ts_first.observation['env_state'])
box_xyz = box_info[:3]
box_quat = box_info[3:]
# print(f"Generate trajectory for {box_xyz=}")
gripper_pick_quat = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat = gripper_pick_quat * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
meet_left_quat = Quaternion(axis=[1.0, 0.0, 0.0], degrees=90)
meet_xyz = np.array([0, 0.5, 0.25])
self.left_trajectory = [
{"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
{"t": 100, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # approach meet position
{"t": 260, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 1}, # move to meet position
{"t": 310, "xyz": meet_xyz + np.array([0.02, 0, -0.02]), "quat": meet_left_quat.elements, "gripper": 0}, # close gripper
{"t": 360, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # move left
{"t": 400, "xyz": meet_xyz + np.array([-0.1, 0, -0.02]), "quat": np.array([1, 0, 0, 0]), "gripper": 0}, # stay
]
self.right_trajectory = [
{"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
{"t": 90, "xyz": box_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat.elements, "gripper": 1}, # approach the cube
{"t": 130, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 1}, # go down
{"t": 170, "xyz": box_xyz + np.array([0, 0, -0.015]), "quat": gripper_pick_quat.elements, "gripper": 0}, # close gripper
{"t": 200, "xyz": meet_xyz + np.array([0.05, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 0}, # approach meet position
{"t": 220, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 0}, # move to meet position
{"t": 310, "xyz": meet_xyz, "quat": gripper_pick_quat.elements, "gripper": 1}, # open gripper
{"t": 360, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # move to right
{"t": 400, "xyz": meet_xyz + np.array([0.1, 0, 0]), "quat": gripper_pick_quat.elements, "gripper": 1}, # stay
]
class InsertionPolicy(BasePolicy):
def generate_trajectory(self, ts_first):
init_mocap_pose_right = ts_first.observation['mocap_pose_right']
init_mocap_pose_left = ts_first.observation['mocap_pose_left']
peg_info = np.array(ts_first.observation['env_state'])[:7]
peg_xyz = peg_info[:3]
peg_quat = peg_info[3:]
socket_info = np.array(ts_first.observation['env_state'])[7:]
socket_xyz = socket_info[:3]
socket_quat = socket_info[3:]
gripper_pick_quat_right = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat_right = gripper_pick_quat_right * Quaternion(axis=[0.0, 1.0, 0.0], degrees=-60)
gripper_pick_quat_left = Quaternion(init_mocap_pose_right[3:])
gripper_pick_quat_left = gripper_pick_quat_left * Quaternion(axis=[0.0, 1.0, 0.0], degrees=60)
meet_xyz = np.array([0, 0.5, 0.15])
lift_right = 0.00715
self.left_trajectory = [
{"t": 0, "xyz": init_mocap_pose_left[:3], "quat": init_mocap_pose_left[3:], "gripper": 0}, # sleep
{"t": 120, "xyz": socket_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # approach the cube
{"t": 170, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 1}, # go down
{"t": 220, "xyz": socket_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # close gripper
{"t": 285, "xyz": meet_xyz + np.array([-0.1, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # approach meet position
{"t": 340, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements,"gripper": 0}, # insertion
{"t": 400, "xyz": meet_xyz + np.array([-0.05, 0, 0]), "quat": gripper_pick_quat_left.elements, "gripper": 0}, # insertion
]
self.right_trajectory = [
{"t": 0, "xyz": init_mocap_pose_right[:3], "quat": init_mocap_pose_right[3:], "gripper": 0}, # sleep
{"t": 120, "xyz": peg_xyz + np.array([0, 0, 0.08]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # approach the cube
{"t": 170, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 1}, # go down
{"t": 220, "xyz": peg_xyz + np.array([0, 0, -0.03]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # close gripper
{"t": 285, "xyz": meet_xyz + np.array([0.1, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # approach meet position
{"t": 340, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion
{"t": 400, "xyz": meet_xyz + np.array([0.05, 0, lift_right]), "quat": gripper_pick_quat_right.elements, "gripper": 0}, # insertion
]
def test_policy(task_name):
# example rolling out pick_and_transfer policy
onscreen_render = True
inject_noise = False
# setup the environment
episode_len = SIM_TASK_CONFIGS[task_name]['episode_len']
if 'sim_transfer_cube' in task_name:
env = make_ee_sim_env('sim_transfer_cube')
elif 'sim_insertion' in task_name:
env = make_ee_sim_env('sim_insertion')
else:
raise NotImplementedError
for episode_idx in range(2):
ts = env.reset()
episode = [ts]
if onscreen_render:
ax = plt.subplot()
plt_img = ax.imshow(ts.observation['images']['angle'])
plt.ion()
policy = PickAndTransferPolicy(inject_noise)
for step in range(episode_len):
action = policy(ts)
ts = env.step(action)
episode.append(ts)
if onscreen_render:
plt_img.set_data(ts.observation['images']['angle'])
plt.pause(0.02)
plt.close()
episode_return = np.sum([ts.reward for ts in episode[1:]])
if episode_return > 0:
print(f"{episode_idx=} Successful, {episode_return=}")
else:
print(f"{episode_idx=} Failed")
if __name__ == '__main__':
test_task_name = 'sim_transfer_cube_scripted'
test_policy(test_task_name)