-
Notifications
You must be signed in to change notification settings - Fork 110
/
panda.py
140 lines (118 loc) · 6.14 KB
/
panda.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
from typing import Optional
import numpy as np
from gym import spaces
from panda_gym.envs.core import PyBulletRobot
from panda_gym.pybullet import PyBullet
class Panda(PyBulletRobot):
"""Panda robot in PyBullet.
Args:
sim (PyBullet): Simulation instance.
block_gripper (bool, optional): Whether the gripper is blocked. Defaults to False.
base_position (np.ndarray, optionnal): Position of the base base of the robot, as (x, y, z). Defaults to (0, 0, 0).
control_type (str, optional): "ee" to control end-effector displacement or "joints" to control joint angles.
Defaults to "ee".
"""
def __init__(
self,
sim: PyBullet,
block_gripper: bool = False,
base_position: Optional[np.ndarray] = None,
control_type: str = "ee",
) -> None:
base_position = base_position if base_position is not None else np.zeros(3)
self.block_gripper = block_gripper
self.control_type = control_type
n_action = 3 if self.control_type == "ee" else 7 # control (x, y z) if "ee", else, control the 7 joints
n_action += 0 if self.block_gripper else 1
action_space = spaces.Box(-1.0, 1.0, shape=(n_action,), dtype=np.float32)
super().__init__(
sim,
body_name="panda",
file_name="franka_panda/panda.urdf",
base_position=base_position,
action_space=action_space,
joint_indices=np.array([0, 1, 2, 3, 4, 5, 6, 9, 10]),
joint_forces=np.array([87.0, 87.0, 87.0, 87.0, 12.0, 120.0, 120.0, 170.0, 170.0]),
)
self.fingers_indices = np.array([9, 10])
self.neutral_joint_values = np.array([0.00, 0.41, 0.00, -1.85, 0.00, 2.26, 0.79, 0.00, 0.00])
self.ee_link = 11
self.sim.set_lateral_friction(self.body_name, self.fingers_indices[0], lateral_friction=1.0)
self.sim.set_lateral_friction(self.body_name, self.fingers_indices[1], lateral_friction=1.0)
self.sim.set_spinning_friction(self.body_name, self.fingers_indices[0], spinning_friction=0.001)
self.sim.set_spinning_friction(self.body_name, self.fingers_indices[1], spinning_friction=0.001)
def set_action(self, action: np.ndarray) -> None:
action = action.copy() # ensure action don't change
action = np.clip(action, self.action_space.low, self.action_space.high)
if self.control_type == "ee":
ee_displacement = action[:3]
target_arm_angles = self.ee_displacement_to_target_arm_angles(ee_displacement)
else:
arm_joint_ctrl = action[:7]
target_arm_angles = self.arm_joint_ctrl_to_target_arm_angles(arm_joint_ctrl)
if self.block_gripper:
target_fingers_width = 0
else:
fingers_ctrl = action[-1] * 0.2 # limit maximum change in position
fingers_width = self.get_fingers_width()
target_fingers_width = fingers_width + fingers_ctrl
target_angles = np.concatenate((target_arm_angles, [target_fingers_width / 2, target_fingers_width / 2]))
self.control_joints(target_angles=target_angles)
def ee_displacement_to_target_arm_angles(self, ee_displacement: np.ndarray) -> np.ndarray:
"""Compute the target arm angles from the end-effector displacement.
Args:
ee_displacement (np.ndarray): End-effector displacement, as (dx, dy, dy).
Returns:
np.ndarray: Target arm angles, as the angles of the 7 arm joints.
"""
ee_displacement = ee_displacement[:3] * 0.05 # limit maximum change in position
# get the current position and the target position
ee_position = self.get_ee_position()
target_ee_position = ee_position + ee_displacement
# Clip the height target. For some reason, it has a great impact on learning
target_ee_position[2] = np.max((0, target_ee_position[2]))
# compute the new joint angles
target_arm_angles = self.inverse_kinematics(
link=self.ee_link, position=target_ee_position, orientation=np.array([1.0, 0.0, 0.0, 0.0])
)
target_arm_angles = target_arm_angles[:7] # remove fingers angles
return target_arm_angles
def arm_joint_ctrl_to_target_arm_angles(self, arm_joint_ctrl: np.ndarray) -> np.ndarray:
"""Compute the target arm angles from the arm joint control.
Args:
arm_joint_ctrl (np.ndarray): Control of the 7 joints.
Returns:
np.ndarray: Target arm angles, as the angles of the 7 arm joints.
"""
arm_joint_ctrl = arm_joint_ctrl * 0.05 # limit maximum change in position
# get the current position and the target position
current_arm_joint_angles = np.array([self.get_joint_angle(joint=i) for i in range(7)])
target_arm_angles = current_arm_joint_angles + arm_joint_ctrl
return target_arm_angles
def get_obs(self) -> np.ndarray:
# end-effector position and velocity
ee_position = np.array(self.get_ee_position())
ee_velocity = np.array(self.get_ee_velocity())
# fingers opening
if not self.block_gripper:
fingers_width = self.get_fingers_width()
obs = np.concatenate((ee_position, ee_velocity, [fingers_width]))
else:
obs = np.concatenate((ee_position, ee_velocity))
return obs
def reset(self) -> None:
self.set_joint_neutral()
def set_joint_neutral(self) -> None:
"""Set the robot to its neutral pose."""
self.set_joint_angles(self.neutral_joint_values)
def get_fingers_width(self) -> float:
"""Get the distance between the fingers."""
finger1 = self.sim.get_joint_angle(self.body_name, self.fingers_indices[0])
finger2 = self.sim.get_joint_angle(self.body_name, self.fingers_indices[1])
return finger1 + finger2
def get_ee_position(self) -> np.ndarray:
"""Returns the position of the ned-effector as (x, y, z)"""
return self.get_link_position(self.ee_link)
def get_ee_velocity(self) -> np.ndarray:
"""Returns the velocity of the end-effector as (vx, vy, vz)"""
return self.get_link_velocity(self.ee_link)