human like sepoa
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
# 改字体大小
plt.rcParams.update({'font.size': 12})
# 不要使用 type 3 字体
plt.rcParams.update({'pdf.fonttype': 42})
# 数据
tasks = ['Cheetah_run', 'Walker_run', 'Quadruped_walk', 'Quadruped_run']
methods = ['Method', 'SURF', 'PEBBLE', 'RUNE']
data = np.array([
[[16, 10, 11, 11], [18, 9, 11, 11], [17, 12, 14, 10]], # Cheetah_run
[[16, 9, 11, 11], [19, 10, 11, 11], [18, 10, 12, 10]], # Walker_run
[[17, 10, 12, 10], [16, 9, 12, 11], [19, 10, 12, 10]], # Quadruped_walk
[[15, 10, 12, 9], [16, 9, 13, 11], [17, 10, 12, 7]] # Quadruped_run
])
data = data * 5
mean_data = np.mean(data, axis=1)
variance = np.var(data, axis=1)
variance = np.sqrt(variance)
# 绘图
fig, ax = plt.subplots(figsize=(7, 2.5))
# 设置条形图的位置和宽度
width = 0.2
true_width = 0.16
x = np.arange(len(tasks)) # x轴的位置
# 绘制条形图
rects1 = ax.bar(x - width, mean_data[:, 0], true_width, label='S-EPOA',
color='mistyrose', edgecolor='indianred', linewidth=1.8)
rects2 = ax.bar(x, mean_data[:, 1], true_width, label='SURF',
color='floralwhite', edgecolor='peru', linewidth=1.8)
rects3 = ax.bar(x + width, mean_data[:, 2], true_width, label='PEBBLE',
color='honeydew', edgecolor='mediumseagreen', linewidth=1.8)
rects4 = ax.bar(x + 2 * width, mean_data[:, 3], true_width, label='RUNE',
color='lavender', edgecolor='mediumpurple', linewidth=1.8)
# 添加误差条（yerr 参数）
ax.errorbar(x - width, mean_data[:, 0], yerr=variance[:, 0], fmt='none', color='indianred', capsize=5)
ax.errorbar(x, mean_data[:, 1], yerr=variance[:, 1], fmt='none', color='peru', capsize=5)
ax.errorbar(x + width, mean_data[:, 2], yerr=variance[:, 2], fmt='none', color='mediumseagreen', capsize=5)
ax.errorbar(x + 2 * width, mean_data[:, 3], yerr=variance[:, 3], fmt='none', color='mediumpurple', capsize=5)
# 添加标签和标题
ax.set_ylabel('Match Ratio (%)', fontsize=14)
# ax.set_title('Performance Comparison by Task and Method')
ax.set_xticks(x + width / 2) # 设置 x 轴标签的位置
# ax.set_xticks(x)
ax.set_xticklabels(tasks, fontname='FreeMono', rotation=0, ha='center')
ax.set_ylim(0, 100)
ax.set_yticks([0, 25, 50, 75, 100])
ax.yaxis.grid(True, color='lightgray', linestyle='-', linewidth=1)
ax.set_axisbelow(True)
lines, labels = fig.axes[-1].get_legend_handles_labels()
fig.legend(lines, labels, bbox_to_anchor=(0.5, 1.02), loc='center',
ncol=4, prop={'size': 13}, frameon=False)
# 显示图形
plt.tight_layout()
# plt.show()
plt.savefig('./human_experiments/human_like_sepoa.png', bbox_inches='tight', pad_inches=0.2)
plt.savefig('./human_experiments/human_like_sepoa.pdf', bbox_inches='tight', pad_inches=0.2)
plt.close()
human get confused (sepoa)
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
# 改字体大小
plt.rcParams.update({'font.size': 12})
# 不要使用 type 3 字体
plt.rcParams.update({'pdf.fonttype': 42})
# 数据
tasks = ['Cheetah_run', 'Walker_run', 'Quadruped_walk', 'Quadruped_run']
return_diff_10 = [53.33, 48.33, 55.83, 56.67] # 对应 10
return_diff_100 = [68.33, 73.33, 63.33, 70] # 对应 100
return_diff_500 = [95, 98.33, 71.67, 73.33] # 对应 500
# 计算上面三行值 / 5 的结果
# return_diff_10 = [11, 10, 11, 11]
# return_diff_100 = [13, 15, 13, 14]
# return_diff_500 = [19, 20, 14, 14]
# 生成条形图的 x 轴位置
x = np.arange(len(tasks)) # 任务数目
width = 0.25 # 条形宽度
true_width = 0.18
# 创建图形
fig, ax = plt.subplots(figsize=(7, 2.5))
# 绘制条形图
rects1 = ax.bar(x - width, return_diff_500, true_width, label='Return Difference (RD) = 500',
color='mistyrose', edgecolor='indianred', linewidth=1.8) # hatch='//'
rects2 = ax.bar(x, return_diff_100, true_width, label='RD = 100',
color='floralwhite', edgecolor='peru', linewidth=1.8) # hatch='\\'
rects3 = ax.bar(x + width, return_diff_10, true_width, label='RD = 10',
color='aliceblue', edgecolor='dodgerblue', linewidth=1.8) # hatch='--'
# 添加一些文本标签、标题和自定义 x 轴标签
ax.set_ylim(0, 100)
ax.set_yticks([0, 25, 50, 75, 100])
ax.set_ylabel('Match Ratio (%)', fontsize=14)
# ax.set_title('Return Differences by Task and Time')
ax.set_xticks(x)
ax.set_xticklabels(tasks, fontname='FreeMono', fontsize=12) # , fontweight='bold'
plt.xticks(rotation=0, ha='center')
ax.yaxis.grid(True, color='lightgray', linestyle='-', linewidth=1)
ax.set_axisbelow(True)
# ax.legend(fontsize=12)
lines, labels = fig.axes[-1].get_legend_handles_labels()
fig.legend(lines, labels, bbox_to_anchor=(0.5, 1.02), loc='center',
ncol=3, prop={'size': 13}, frameon=False)
# 给条形图添加数值标签
def add_labels(rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height}%',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
# 为每一组条形图添加标签
# add_labels(rects1)
# add_labels(rects2)
# add_labels(rects3)
# 显示图形
plt.tight_layout()
# plt.show()
plt.savefig('./human_experiments/human_get_confused.png', bbox_inches='tight', pad_inches=0.2)
plt.savefig('./human_experiments/human_get_confused.pdf', bbox_inches='tight', pad_inches=0.2)
plt.close()
human like clarify
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
# 改字体大小
plt.rcParams.update({'font.size': 14})
# 不要使用 type 3 字体
plt.rcParams.update({'pdf.fonttype': 42})
# 数据
method_name = 'CLARITY'
tasks = ['dial-turn', 'hammer', 'walker-walk']
methods = [method_name, 'OPRL']
base_idx = 0
skip_ratio_mean = {
method_name: [0.183333333, 0.25, 0.3],
'OPRL': [0.5, 0.333333333, 0.416666667],
}
skip_ratio_stderr = {
method_name: [0.047140452, 0.081649658, 0.147196014],
'OPRL': [0.035355339, 0.023570226, 0.131233465],
}
error_ratio_mean = {
method_name: [0.2, 0.2, 0.183333333],
'OPRL': [0.2125, 0.266666667, 0.35],
}
error_ratio_stderr = {
method_name: [0.141421356, 0.147196014, 0.084983659],
'OPRL': [0.04145781, 0.023570226, 0.227303028],
}
skip_mean_data = 100 - np.array([
skip_ratio_mean[method_name],
skip_ratio_mean['OPRL'],
]).T * 100.0 # (3, 2)
skip_stderr = np.array([
skip_ratio_stderr[method_name],
skip_ratio_stderr['OPRL'],
]).T * 100.0
error_mean_data = 100 - np.array([
error_ratio_mean[method_name],
error_ratio_mean['OPRL'],
]).T * 100.0 # (3, 2)
error_stderr = np.array([
error_ratio_stderr[method_name],
error_ratio_stderr['OPRL'],
]).T * 100.0
# 绘图
fig, ax = plt.subplots(figsize=(4.5, 3))
# 设置条形图的位置和宽度
width = 0.4
true_width = 0.3
x = np.arange(len(tasks)) # x轴的位置
# 绘制条形图
# rects1 = ax.bar(x, skip_mean_data[:, 0], true_width, label=method_name ,
# color='mistyrose', edgecolor='indianred', linewidth=1.8)
# rects2 = ax.bar(x + width, skip_mean_data[:, 1], true_width, label='OPRL',
# color='floralwhite', edgecolor='peru', linewidth=1.8)
# ax.errorbar(x, skip_mean_data[:, 0], yerr=skip_stderr[:, 0], fmt='none', color='indianred', capsize=5, linewidth=1.4)
# ax.errorbar(x + width, skip_mean_data[:, 1], yerr=skip_stderr[:, 1], fmt='none', color='peru', capsize=5, linewidth=1.4)
# ax.set_ylabel('Query clarity ratio (%)', fontsize=16)
rects1 = ax.bar(x, error_mean_data[:, 0], true_width, label=method_name,
color='honeydew', edgecolor='mediumseagreen', linewidth=1.8)
rects2 = ax.bar(x + width, error_mean_data[:, 1], true_width, label='OPRL',
color='lavender', edgecolor='mediumpurple', linewidth=1.8)
ax.errorbar(x, error_mean_data[:, 0], yerr=error_stderr[:, 0], fmt='none', color='mediumseagreen', capsize=5, linewidth=1.4)
ax.errorbar(x + width, error_mean_data[:, 1], yerr=error_stderr[:, 1], fmt='none', color='mediumpurple', capsize=5, linewidth=1.4)
ax.set_ylabel('Accuracy (%)', fontsize=16)
# 添加标签和标题
ax.set_xticks(x + width / 2) # 设置 x 轴标签的位置
# ax.set_xticks(x)
ax.set_xticklabels(tasks, rotation=0, ha='center')
ax.set_ylim(20, 100)
ax.set_yticks([20, 50, 75, 100])
ax.yaxis.grid(True, color='lightgray', linestyle='-', linewidth=1)
ax.set_axisbelow(True)
lines, labels = fig.axes[-1].get_legend_handles_labels()
fig.legend(lines, labels, bbox_to_anchor=(0.5, 1.02), loc='center',
ncol=4, prop={'size': 16}, frameon=False)
# 显示图形
plt.tight_layout()
# plt.show()
plt.savefig('./human_exp/human_like_pbhim.png', bbox_inches='tight', pad_inches=0.2)
plt.savefig('./human_exp/human_like_pbhim.pdf', bbox_inches='tight', pad_inches=0.2)
plt.close()
human get confused (clarify)
import matplotlib.pyplot as plt
import numpy as np
color_list = [
['mistyrose', 'indianred'],
['floralwhite', 'peru'],
['aliceblue', 'dodgerblue'],
['honeydew', 'mediumseagreen'],
['lavender', 'mediumpurple'],
['lavenderblush', 'violet'],
]
if __name__ == '__main__':
# 改字体大小
plt.rcParams.update({'font.size': 14})
# 不要使用 type 3 字体
plt.rcParams.update({'pdf.fonttype': 42})
# 数据
tasks = ['dial-turn', 'hammer', 'walker-walk']
skip_ratio_mean = { # 10, 100, 300
'dial-turn': [71.67, 73.33, 91.67],
'hammer': [58.33, 91.67, 98.33],
'walker-walk': [45.00, 93.33, 100.00],
}
skip_ratio_stderr = {
'dial-turn': [14.34, 2.36, 2.36],
'hammer': [2.36, 4.71, 2.36],
'walker-walk': [10.80, 2.36, 0.00],
}
error_ratio_mean = {
'dial-turn': [72.70, 77.56, 96.39],
'hammer': [83.08, 89.27, 100.00],
'walker-walk': [100.00, 91.13, 100.00],
}
error_ratio_stderr = {
'dial-turn': [9.89, 5.52, 2.55],
'hammer': [6.50, 4.05, 0.00],
'walker-walk': [0.00, 2.34, 0.00],
}
# 生成条形图的 x 轴位置
x = np.arange(len(tasks)) # 任务数目
width = 0.26 # 条形宽度
true_width = 0.2
# 创建图形
fig, ax = plt.subplots(figsize=(6.4, 2.2))
# 绘制条形图
rects1 = ax.bar(x - width, [skip_ratio_mean[ta][2] for ta in tasks], true_width, label='Large Return Difference (RD)',
color=color_list[0][0], edgecolor=color_list[0][1], linewidth=1.8) # hatch='//'
rects2 = ax.bar(x, [skip_ratio_mean[ta][1] for ta in tasks], true_width, label='Medium RD',
color=color_list[1][0], edgecolor=color_list[1][1], linewidth=1.8) # hatch='\\'
rects3 = ax.bar(x + width, [skip_ratio_mean[ta][0] for ta in tasks], true_width, label='Small RD',
color=color_list[2][0], edgecolor=color_list[2][1], linewidth=1.8) # hatch='--'
ax.errorbar(x - width, [skip_ratio_mean[ta][2] for ta in tasks], yerr=[skip_ratio_stderr[ta][2] for ta in tasks],
fmt='none', color=color_list[0][1], capsize=5, linewidth=1.4)
ax.errorbar(x, [skip_ratio_mean[ta][1] for ta in tasks], yerr=[skip_ratio_stderr[ta][1] for ta in tasks],
fmt='none', color=color_list[1][1], capsize=5, linewidth=1.4)
ax.errorbar(x + width, [skip_ratio_mean[ta][0] for ta in tasks], yerr=[skip_ratio_stderr[ta][0] for ta in tasks],
fmt='none', color=color_list[2][1], capsize=5, linewidth=1.4)
ax.set_ylim(20, 100)
ax.set_yticks([20, 50, 75, 100])
ax.set_ylabel('Query clarity ratio (%)', fontsize=14)
# rects1 = ax.bar(x - width, [error_ratio_mean[ta][2] for ta in tasks], true_width, label='Large Return Difference (RD)',
# color=color_list[3][0], edgecolor=color_list[3][1], linewidth=1.8) # hatch='//'
# rects2 = ax.bar(x, [error_ratio_mean[ta][1] for ta in tasks], true_width, label='Medium RD',
# color=color_list[4][0], edgecolor=color_list[4][1], linewidth=1.8) # hatch='\\'
# rects3 = ax.bar(x + width, [error_ratio_mean[ta][0] for ta in tasks], true_width, label='Small RD',
# color=color_list[5][0], edgecolor=color_list[5][1], linewidth=1.8) # hatch='--'
# ax.errorbar(x - width, [error_ratio_mean[ta][2] for ta in tasks], yerr=[error_ratio_stderr[ta][2] for ta in tasks],
# fmt='none', color=color_list[3][1], capsize=5, linewidth=1.4)
# ax.errorbar(x, [error_ratio_mean[ta][1] for ta in tasks], yerr=[error_ratio_stderr[ta][1] for ta in tasks],
# fmt='none', color=color_list[4][1], capsize=5, linewidth=1.4)
# ax.errorbar(x + width, [error_ratio_mean[ta][0] for ta in tasks], yerr=[error_ratio_stderr[ta][0] for ta in tasks],
# fmt='none', color=color_list[5][1], capsize=5, linewidth=1.4)
# ax.set_ylim(60, 100)
# ax.set_yticks([60, 80, 100])
# ax.set_ylabel('Accuracy (%)', fontsize=16)
ax.set_xticks(x)
ax.set_xticklabels(tasks, ha='center', fontsize=16) # , fontweight='bold'
ax.yaxis.grid(True, color='lightgray', linestyle='-', linewidth=1)
ax.set_axisbelow(True)
# ax.legend(fontsize=12)
lines, labels = fig.axes[-1].get_legend_handles_labels()
labels = labels[:2] + [''] + labels[2:]
lines = lines[:2] + [plt.Line2D([0], [0], color='white', lw=0)] + lines[2:]
fig.legend(lines, labels, bbox_to_anchor=(0.5, 1.02), loc='center',
ncol=2, prop={'size': 14}, frameon=False)
# 显示图形
plt.tight_layout()
# plt.show()
plt.savefig('./human_exp/human_get_confused.png', bbox_inches='tight', pad_inches=0.2)
plt.savefig('./human_exp/human_get_confused.pdf', bbox_inches='tight', pad_inches=0.2)
plt.close()
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