n=5,
radius_factor=0.50)
graphs.hold(False)
# update m_channels
update_m_channels(env, ex, m_center, ranges[idx])
min_y, min_idx = 0, -1
for i, (x, y) in enumerate(s_vectors):
if y < min_y:
min_y = y
min_idx = i
# making graphs
graphs.spread(env.s_channels,
s_vectors=s_vectors,
e_radius=1.5,
e_alpha=0.25,
plot_height=250,
plot_width=800,
y_range=[-1, 0.05],
x_range=[-0.2, 0.1],
title='{}::{}'.format(ex_name, env_name))
graphs.hold(True)
graphs.posture_signals(env,
[explorations[min_idx][0]['m_signal']],
alpha=0.75,
radius_factor=0.35)
graphs.show()
s_vectors0 = []
for explo in explorations[:MB]:
feedback = env0.execute(explo[0]['m_signal'])
s_vectors0.append(
explorers.tools.to_vector(feedback['s_signal'], env0.s_channels))
graphs.spread(env.s_channels,
s_vectors=s_vectors0[:MB],
e_radius=3.0,
e_alpha=1.0,
e_color='#DF6464',
title='first arm - reused effects')
for e in explorations[:MB]:
graphs.hold(True)
graphs.posture_signals(env0, [e[0]['m_signal']],
alpha=ARM_ALPHA,
radius_factor=0.75)
graphs.hold(True)
graphs.spread(env.s_channels,
s_vectors=s_vectors0[:MB],
grid=False,
e_radius=3.0,
e_alpha=1.0,
e_color='#DF6464')
graphs.hold(False)
# Graph Reuse
for t in [MB, 200, 400, N]:
# alpha = 1.0 if t != N else 0.5
graphs.bokeh_spread(env.s_channels,
# Splitting the trials
s_vectors_pos, s_vectors_neg = [], []
for explo, s_vector in zip(explorations, s_vectors):
if explo[0]['m_signal']['j0'] > 0:
s_vectors_pos.append(s_vector)
else:
s_vectors_neg.append(s_vector)
graphs.output_file('c0_fig4_kin2_decomp.html')
# Positive Graph
graphs.spread(env.s_channels, s_vectors=s_vectors_pos,
e_radius=1.0, e_alpha=0.5,
title='{}::{} [positive]'.format(env_name, ex_name))
graphs.hold(True)
graphs.posture_signals(env, [{'j0': i, 'j1':-150+2*i} for i in range(150, -1, -10)],
color='#333333', alpha=0.5, swap_xy=True)
graphs.hold(False)
# Negative Graph
graphs.spread(env.s_channels, s_vectors=s_vectors_neg,
e_radius=1.0, e_alpha=0.5,
title='{}::{} [negative]'.format(env_name, ex_name))
graphs.hold(True)
graphs.posture_signals(env, [{'j0': -i, 'j1':-150+2*i} for i in range(0, 151, 10)],
color='#333333', alpha=0.5, swap_xy=True)
graphs.hold(False)
graphs.show()
# running the exploration
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N)
# making graphs
for t in milestones:
idxs = []
for x_ in range(10):
idxs.append(
find_rightmost(s_vectors[:t], -x_ / 10.0,
-(x_ - 1) / 10.0))
idxs.append(find_rightmost(s_vectors[:t], -0.960, -0.940))
idxs.append(find_rightmost(s_vectors[:t], -0.940, -0.920))
idxs.append(find_rightmost(s_vectors[:t], -0.920, -0.900))
idxs.append(find_rightmost(s_vectors[:t], -0.900, -0.850))
graphs.spread(env.s_channels,
s_vectors=s_vectors[:t],
e_radius=1.4,
e_alpha=0.5,
title='{}::{}::{}'.format(ex_name, env_name, t))
for idx in idxs:
graphs.hold(True)
graphs.posture_signals(env, [explorations[idx][0]['m_signal']],
color='#666666',
alpha=0.50,
radius_factor=0.6)
graphs.show()
# 'Self-Exploration of Sensorimotor Spaces in Robots' by Fabien C. Y. Benureau
# Licensed under the Open Science License (see http://fabien.benureau.com/openscience.html)
import environments
import dotdot
import envs
import graphs
# instanciating the environment
env_cfg = envs.catalog['kin7_150']._deepcopy()
env = environments.Environment.create(env_cfg)
# defining postures
m_vectors = [
(132.654, -108.829, -100.829, 12.724, 133.011, -76.348, -118.995),
(-19.163, 69.503, 44.059, -88.545, -36.027, 76.281, 8.720),
(138.239, 39.661, -84.211, 15.735, -33.844, 104.375, 26.110),
(19.678, -95.130, 96.038, -7.118, 56.496, 10.632, 111.627),
(135.685, 70.589, -69.667, -7.244, 149.627, -42.752, -54.894),
]
m_signals = [
environments.tools.to_signal(m_vector, env.m_channels)
for m_vector in m_vectors
]
# making graphs
graphs.output_file('c0_fig1_kin7_example.html')
graphs.posture_signals(env, m_signals, color='#666666', alpha=0.5)
graphs.show()
# making graphs
milestones = [0, 100, 200, 500, 1000, 2000, 5000, N]
for t in milestones[1:]:
alpha = 0.5 if t == 100 else 0.25
graphs.spread(ex.s_channels,
s_vectors=s_vectors[:t],
s_goals=(),
e_radius=2.0,
e_alpha=alpha,
x_range=(-1.05, 1.05),
y_range=(-1.05, 1.05),
title='demo:{}, t={}'.format(env_name, t))
graphs.hold(True)
graphs.posture_signals(env,
dataset['m_signals'],
radius_factor=0.35,
alpha=0.75)
graphs.hold(True)
graphs.spread(ex.s_channels,
s_vectors=s_vectors[:1],
s_goals=(),
e_radius=2.0,
e_alpha=.0,
e_color='#DF6464')
graphs.hold(True)
graphs.posture_random(env,
explorations[milestones[milestones.index(t) -
1]:],
n=10,
alpha=0.75,
radius_factor=0.35)
graphs.output_file('c0_fig6_kin2_gb.html'.format(int(N/1000)))
random.seed(0)
# instanciating the environment
env_cfg = envs.catalog[env_name]._deepcopy()
env = environments.Environment.create(env_cfg)
# instanciating the explorer
ex_cfg = exs.catalog[ex_name]._deepcopy()
ex_cfg.m_channels = env.m_channels
ex_cfg.s_channels = env.s_channels
ex = explorers.Explorer.create(ex_cfg)
# running the Exploration
explorations, s_vectors, s_goals = factored.run_exploration(env, ex, N)
# making graphs
graphs.spread(env.s_channels, s_vectors=s_vectors,
e_radius=1.0, e_alpha=0.25,
title='{}::{}'.format(ex_name, env_name))
graphs.hold(True)
m_vectors = [(150, 103), (135, 110), ( 0, -45), ( 15, -50)]
m_signals = [environments.tools.to_signal(m_vector, env.m_channels) for m_vector in m_vectors]
graphs.posture_signals(env, m_signals, color='#333333', alpha=1.0)
graphs.show()
import dotdot
import envs
import graphs
# instanciating the environment
env_cfg = envs.catalog['kin2_150']._deepcopy()
env = environments.Environment.create(env_cfg)
# making graphs
graphs.output_file('c0_fig1_kin2_ranges.html')
for a in range(-150, 150, 10):
graphs.posture_signals(env, [{
'j0': 0,
'j1': a
}],
color='#333333',
alpha=a * 0.0005 + 0.3,
title='joint ranges')
graphs.hold(True)
for a in range(0, 151, 15):
graphs.posture_signals(env, [{
'j0': a,
'j1': 150
}],
color='#666666',
alpha=0.5)
graphs.hold(True)
graphs.show()
selected = [m_signals[i] for i in [0, 1, 2, 3, 5, 9]]
# spreading their first joints to avoid superpositions
m_signals[0]['j{}'.format(DIM - 1)] = 100.0
m_signals[1]['j{}'.format(DIM - 1)] = -150.0
m_signals[2]['j{}'.format(DIM - 1)] = -30.0
m_signals[3]['j{}'.format(DIM - 1)] = 50.0
m_signals[5]['j{}'.format(DIM - 1)] = -50.0
m_signals[9]['j{}'.format(DIM - 1)] = 20.0
# making graphs
graphs.output_file('c5_fig5_1_examples.html')
for kin_env, color in zip([env, env2], ['#666666', '#E84A5F']):
graphs.posture_signals(kin_env,
m_signals,
color=color,
alpha=0.1,
radius_factor=0.5)
graphs.hold(True)
for kin_env, color in zip([env, env2], ['#666666', '#E84A5F']):
graphs.posture_signals(kin_env,
selected,
color=color,
alpha=0.75,
radius_factor=0.5)
graphs.hold(True)
graphs.hold(False)
graphs.posture_signals(env, [{'j{}'.format(i): 0
for i in range(20)}],
color='#666666',
