mult-skill-act-models / normalization /point5_v30_norm.py
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"""
归一化常量 - point5_v30
从 data/each-point-V30/point5_v30/meta/stats.json 自动生成
"""
import numpy as np
# ====== 归一化统计量(从 point5_v30 数据集提取)======
# State 归一化参数 (observation.state) - 14D
STATE_MEAN = np.array([
-0.28005248155530416,
1.3383875955120301,
-1.1340355494467393,
0.04400213425097945,
0.6931494163132652,
-0.013576608081085108,
0.05974055656493036,
0.018501200326071592,
-0.03282953766787461,
-0.15235542188725504,
-0.002301428052642584,
0.5936170996343659,
-0.04125813545263295,
0.0
], dtype=np.float32)
STATE_STD = np.array([
0.21416834336270812,
0.9030553839265362,
0.6935661050312355,
0.06133690093023171,
0.19034198074473987,
0.09296174655805599,
0.0318032495403896,
0.020133542034951886,
3.9235387410912323e-05,
0.0004789654057632508,
0.002848207154696946,
0.0019173978824850602,
0.007214434067294394,
0.0
], dtype=np.float32)
# Action 反归一化参数 - 14D (left_arm 7D + right_arm 7D)
ACTION_MEAN = np.array([
-0.2807868087526399,
1.3186104217237944,
-1.1624275894222404,
0.04484921323885086,
0.6214815075819917,
-0.01548597917326217,
0.05852798315059126,
0.018503104133437743,
-0.03181775955729813,
-0.17297348977113092,
-0.00583117052590501,
0.5048373769527363,
-0.04608024321727365,
-0.021100036575480336
], dtype=np.float32)
ACTION_STD = np.array([
0.21595120233546664,
0.8836377848472635,
0.6972781236780935,
0.06790456168072799,
0.20456011389450912,
0.0950634453821984,
0.03626923789601678,
0.018581566641577462,
8.614682487762797e-05,
0.0009287712234452638,
0.0005459503451593117,
0.0003580720599472833,
0.00417338909340118,
4.801666942775271e-06
], dtype=np.float32)
# Image 归一化参数(ImageNet 标准)
IMAGE_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGE_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)