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grasp.py
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355 lines (251 loc) · 16.3 KB
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import BehavioralOrganization as BehOrg
import DynamicField
import Kernel
import numpy
import math
import matplotlib.pyplot as plt
from matplotlib import rc
from enthought.mayavi import mlab
from mpl_toolkits.axes_grid import ImageGrid
from mpl_toolkits.axes_grid import make_axes_locatable
import plot_settings
import math_tools
import CameraField
import EndEffectorControl
def main():
grasp_architecture = BehOrg.GraspArchitecture()
time_steps = 300
task_node_activation = [0] * time_steps
find_color_intention_node_activation = [0] * time_steps
find_color_cos_node_activation = [0] * time_steps
find_color_cos_memory_node_activation = [0] * time_steps
find_color_intention_field_activation = numpy.zeros((time_steps, grasp_architecture._find_color_field_size))
find_color_cos_field_activation = numpy.zeros((time_steps, grasp_architecture._find_color_field_size))
move_ee_intention_node_activation = [0] * time_steps
move_ee_cos_node_activation = [0] * time_steps
move_ee_cos_memory_node_activation = [0] * time_steps
move_ee_intention_field_activation = numpy.zeros((time_steps, grasp_architecture._move_ee_field_sizes[0]))
move_ee_cos_field_activation = numpy.zeros((time_steps, grasp_architecture._move_ee_field_sizes[0]))
gripper_open_intention_node_activation = [0] * time_steps
gripper_open_cos_node_activation = [0] * time_steps
gripper_open_cos_memory_node_activation = [0] * time_steps
gripper_close_intention_node_activation = [0] * time_steps
gripper_close_cos_node_activation = [0] * time_steps
gripper_close_cos_memory_node_activation = [0] * time_steps
gripper_intention_field_activation = numpy.zeros((time_steps, grasp_architecture._gripper_field_size))
gripper_cos_field_activation = numpy.zeros((time_steps, grasp_architecture._gripper_field_size))
gripper_open_precondition_node_activation = [0] * time_steps
gripper_close_precondition_node_activation = [0] * time_steps
color_space_field_x_activation = numpy.zeros((time_steps, grasp_architecture._color_space_field_sizes[0]))
color_space_field_y_activation = numpy.zeros((time_steps, grasp_architecture._color_space_field_sizes[1]))
color_space_field_hue_activation = numpy.zeros((time_steps, grasp_architecture._color_space_field_sizes[2]))
camera_field_x_activation = numpy.zeros((time_steps, grasp_architecture._camera_field_sizes[0]))
camera_field_y_activation = numpy.zeros((time_steps, grasp_architecture._camera_field_sizes[1]))
camera_field_hue_activation = numpy.zeros((time_steps, grasp_architecture._camera_field_sizes[2]))
spatial_target_field_x_activation = numpy.zeros((time_steps, grasp_architecture._spatial_target_field_sizes[0]))
spatial_target_field_y_activation = numpy.zeros((time_steps, grasp_architecture._spatial_target_field_sizes[1]))
perception_ee_field_x_activation = numpy.zeros((time_steps, grasp_architecture._perception_ee_field_sizes[0]))
gripper_boost = math_tools.gauss_1d(grasp_architecture._gripper_field_size, amplitude=10.0, sigma=0.5, shift=grasp_architecture._gripper_field_size-5)
grasp_architecture._gripper_open.get_cos_field().set_boost(gripper_boost)
# perception_ee_boost = math_tools.gauss_2d(perception_ee_field_sizes, amplitude=8.0, sigmas=[2.0, 2.0], shifts=[20,5])
# perception_ee_field.set_boost(perception_ee_boost)
for i in range(time_steps):
print "time step: ", str(i)
if (i == 400):
perception_ee_boost = math_tools.gauss_2d(grasp_architecture._perception_ee_field_sizes, amplitude=8.0, sigmas=[0.5, 0.5], shifts=[5,25])
grasp_architecture._perception_ee_field.set_boost(perception_ee_boost)
# step all connectables and behaviors
grasp_architecture.step()
# save task node activation
task_node_activation[i] = grasp_architecture._task_node.get_activation()[0]
# save find color activations
find_color_intention_node_activation[i] = grasp_architecture._find_color.get_intention_node().get_activation()[0]
find_color_cos_node_activation[i] = grasp_architecture._find_color.get_cos_node().get_activation()[0]
find_color_cos_memory_node_activation[i] = grasp_architecture._find_color.get_cos_memory_node().get_activation()[0]
find_color_intention_field_activation[i] = grasp_architecture._find_color.get_intention_field().get_activation()
find_color_cos_field_activation[i] = grasp_architecture._find_color.get_cos_field().get_activation()
# save move end effector activations
move_ee_intention_node_activation[i] = grasp_architecture._move_ee.get_intention_node().get_activation()[0]
move_ee_cos_node_activation[i] = grasp_architecture._move_ee.get_cos_node().get_activation()[0]
move_ee_cos_memory_node_activation[i] = grasp_architecture._move_ee.get_cos_memory_node().get_activation()[0]
move_ee_intention_field_activation[i] = grasp_architecture._move_ee.get_intention_field().get_activation().max(1)
move_ee_cos_field_activation[i] = grasp_architecture._move_ee.get_cos_field().get_activation().max(1)
# save gripper open activations
gripper_open_intention_node_activation[i] = grasp_architecture._gripper_open.get_intention_node().get_activation()[0]
gripper_open_cos_node_activation[i] = grasp_architecture._gripper_open.get_cos_node().get_activation()[0]
gripper_open_cos_memory_node_activation[i] = grasp_architecture._gripper_open.get_cos_memory_node().get_activation()[0]
# save gripper close activations
gripper_close_intention_node_activation[i] = grasp_architecture._gripper_close.get_intention_node().get_activation()[0]
gripper_close_cos_node_activation[i] = grasp_architecture._gripper_close.get_cos_node().get_activation()[0]
gripper_close_cos_memory_node_activation[i] = grasp_architecture._gripper_close.get_cos_memory_node().get_activation()[0]
# save gripper field activations
gripper_intention_field_activation[i] = grasp_architecture._gripper_intention_field.get_activation()
gripper_cos_field_activation[i] = grasp_architecture._gripper_cos_field.get_activation()
# save precondition activations
gripper_open_precondition_node_activation[i] = grasp_architecture._gripper_open_precondition_node.get_activation()[0]
gripper_close_precondition_node_activation[i] = grasp_architecture._gripper_close_precondition_node.get_activation()[0]
# save color space field activations
color_space_field_hue_x_activation = grasp_architecture._color_space_field.get_activation().max(1)
color_space_field_hue_activation[i] = color_space_field_hue_x_activation.max(0)
color_space_field_x_activation[i] = color_space_field_hue_x_activation.max(1)
color_space_field_y_activation[i] = grasp_architecture._color_space_field.get_activation().max(0).max(1)
# save camera field activations
camera_field_hue_x_activation = grasp_architecture._camera_field.get_activation().max(1)
camera_field_hue_activation[i] = camera_field_hue_x_activation.max(0)
camera_field_x_activation[i] = camera_field_hue_x_activation.max(1)
camera_field_y_activation[i] = grasp_architecture._camera_field.get_activation().max(0).max(1)
# save spatial target activations
spatial_target_field_x_activation[i] = grasp_architecture._spatial_target_field.get_activation().max(1)
spatial_target_field_y_activation[i] = grasp_architecture._spatial_target_field.get_activation().max(0)
# save perception end effector activations
perception_ee_field_x_activation[i] = grasp_architecture._perception_ee_field.get_activation().max(1)
plot_settings.set_mode("icdl")
# create a figure for the "find color" plots
fig = plt.figure(1)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.plot(task_node_activation, 'y-', label=r'task')
plt.plot(find_color_intention_node_activation, 'r-', label=r'fc intention', antialiased=True)
plt.plot(find_color_cos_node_activation, 'b-', label=r'fc cos', antialiased=True)
plt.plot(find_color_cos_memory_node_activation, 'c-', label=r'fc cos mem', antialiased=True)
plt.plot(gripper_open_precondition_node_activation, 'g-.', label=r'open precondition', antialiased=True)
plt.plot(gripper_close_precondition_node_activation, 'm-.', label=r'close precondition', antialiased=True)
plt.legend(loc='upper right')
grid = ImageGrid(fig, 212, nrows_ncols = (2,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(find_color_intention_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,grasp_architecture._find_color_field_size+10,20))
grid[0].set_ylabel(r'fc int')
grid[1].imshow(numpy.rollaxis(find_color_cos_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,grasp_architecture._find_color_field_size+10,20))
grid[1].set_ylabel(r'fc cos')
grid[1].set_xlabel(r'time steps')
grid[1].set_xticks(range(0,time_steps+100,200))
plt.savefig("fig/find_color.pdf", format="pdf")
##########################################################################
# create a figure for the "move ee" plots
fig = plt.figure(2)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.plot(move_ee_intention_node_activation, 'r-', label=r'mee intention', antialiased=True)
plt.plot(move_ee_cos_node_activation, 'b-', label=r'mee cos', antialiased=True)
plt.plot(move_ee_cos_memory_node_activation, 'c-', label=r'mee cos mem', antialiased=True)
plt.legend(loc='upper right')
grid = ImageGrid(fig, 212, nrows_ncols = (5,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(move_ee_intention_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,grasp_architecture._move_ee_field_sizes[0]+10,20))
grid[0].set_ylabel(r'mee int')
grid[1].imshow(numpy.rollaxis(move_ee_cos_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,grasp_architecture._move_ee_field_sizes[0]+10,20))
grid[1].set_ylabel(r'mee cos')
grid[2].imshow(numpy.rollaxis(spatial_target_field_x_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[2].invert_yaxis()
grid[2].set_yticks(range(0,grasp_architecture._spatial_target_field_sizes[0]+10,20))
grid[2].set_ylabel(r'st x')
grid[3].imshow(numpy.rollaxis(spatial_target_field_y_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[3].invert_yaxis()
grid[3].set_yticks(range(0,grasp_architecture._spatial_target_field_sizes[1]+10,20))
grid[3].set_ylabel(r'st y')
grid[4].imshow(numpy.rollaxis(perception_ee_field_x_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[4].invert_yaxis()
grid[4].set_yticks(range(0,grasp_architecture._perception_ee_field_sizes[0]+10,20))
grid[4].set_ylabel(r'pe x')
grid[4].set_xlabel(r'time steps')
grid[4].set_xticks(range(0,time_steps+100,200))
plt.savefig("fig/move_ee.pdf", format="pdf")
##########################################################################
# create a figure for the "gripper" plots
fig = plt.figure(3)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.plot(gripper_open_intention_node_activation, 'r-', label=r'go intention', antialiased=True)
plt.plot(gripper_open_cos_node_activation, 'b-', label=r'go cos', antialiased=True)
plt.plot(gripper_open_cos_memory_node_activation, 'c-', label=r'go cos mem', antialiased=True)
plt.plot(gripper_close_intention_node_activation, 'r--', label=r'gc intention', antialiased=True)
plt.plot(gripper_close_cos_node_activation, 'b--', label=r'gc cos', antialiased=True)
plt.plot(gripper_close_cos_memory_node_activation, 'c--', label=r'gc cos mem', antialiased=True)
plt.legend(loc='upper right')
grid = ImageGrid(fig, 212, nrows_ncols = (2,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(gripper_intention_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,grasp_architecture._gripper_field_size+10,20))
grid[0].set_ylabel(r'go int')
grid[1].imshow(numpy.rollaxis(gripper_cos_field_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,grasp_architecture._gripper_field_size+10,20))
grid[1].set_ylabel(r'go cos')
grid[1].set_xlabel(r'time steps')
grid[1].set_xticks(range(0,time_steps+100,200))
plt.savefig("fig/gripper.pdf", format="pdf")
##########################################################################
# create a figure for the color space field plots
fig = plt.figure(4)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.plot(find_color_intention_node_activation, 'r-', label=r'fc intention', antialiased=True)
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.legend(loc='upper right')
grid = ImageGrid(fig, 212, nrows_ncols = (3,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(color_space_field_x_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,grasp_architecture._color_space_field_sizes[0]+10,20))
grid[0].set_ylabel(r'cs x')
grid[1].imshow(numpy.rollaxis(color_space_field_y_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,grasp_architecture._color_space_field_sizes[1]+10,20))
grid[1].set_ylabel(r'cs y')
grid[2].imshow(numpy.rollaxis(color_space_field_hue_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[2].invert_yaxis()
grid[2].set_yticks(range(0,grasp_architecture._color_space_field_sizes[2]+10,20))
grid[2].set_ylabel(r'cs hue')
grid[2].set_xlabel(r'time steps')
grid[2].set_xticks(range(0,time_steps+100,200))
plt.savefig("fig/color_space.pdf", format="pdf")
##########################################################################
# create a figure for the camera field plots
fig = plt.figure(5)
fig.subplots_adjust(bottom=0.07, left=0.07, right=0.97, top=0.93)
plt.axes([0.125,0.2,0.95-0.125,0.95-0.2])
time_course_subplot = plt.subplot(2,1,1)
time_course_subplot.axes.grid(color='grey', linestyle='dotted')
plt.plot(find_color_intention_node_activation, 'r-', label=r'fc intention', antialiased=True)
plt.xlabel(r'time steps')
plt.ylabel(r'activation')
plt.legend(loc='upper right')
grid = ImageGrid(fig, 212, nrows_ncols = (3,1), axes_pad=0.1, aspect=False)
grid[0].imshow(numpy.rollaxis(camera_field_x_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[0].invert_yaxis()
grid[0].set_yticks(range(0,grasp_architecture._camera_field_sizes[0]+10,20))
grid[0].set_ylabel(r'cam x')
grid[1].imshow(numpy.rollaxis(camera_field_y_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[1].invert_yaxis()
grid[1].set_yticks(range(0,grasp_architecture._camera_field_sizes[1]+10,20))
grid[1].set_ylabel(r'cam y')
grid[2].imshow(numpy.rollaxis(camera_field_hue_activation, 1), aspect="auto", vmin=-10, vmax=10)
grid[2].invert_yaxis()
grid[2].set_yticks(range(0,grasp_architecture._camera_field_sizes[2]+10,20))
grid[2].set_ylabel(r'cam hue')
grid[2].set_xlabel(r'time steps')
grid[2].set_xticks(range(0,time_steps+100,200))
plt.savefig("fig/camera.pdf", format="pdf")
plt.show()
if __name__ == "__main__":
main()