mult-skill-act-models / normalization /point10_v30_norm.py
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"""
归一化常量 - point10_v30
从 data/each-point-V30/point10_v30/meta/stats.json 自动生成
"""
import numpy as np
# ====== 归一化统计量(从 point10_v30 数据集提取)======
# State 归一化参数 (observation.state) - 14D
STATE_MEAN = np.array([
0.03945469130938774,
-0.036684750175496716,
-0.14420977835076654,
0.07125858545528015,
0.6045667835144658,
-0.12207297297360067,
0.0,
0.14661628825205947,
1.232220075192642,
-1.0148434059932745,
-0.016941798111343585,
0.8241991666316403,
0.14750699481995927,
0.052436080354416915
], dtype=np.float32)
STATE_STD = np.array([
0.007826673888420833,
6.507213128181688e-05,
7.196581945487117e-08,
0.00016114506768798135,
0.0010394814327028597,
0.00014708015693177227,
0.0,
0.28313673258313937,
0.8379319205720088,
0.5981789470443272,
0.08554423150673869,
0.2096234444893565,
0.11766143021209088,
0.027404202199854993
], dtype=np.float32)
# Action 反归一化参数 - 14D (left_arm 7D + right_arm 7D)
ACTION_MEAN = np.array([
0.04208625927977355,
-0.02820690410301247,
-0.17410934984246795,
0.06721168250064102,
0.507706056050683,
-0.12866700771276113,
-0.01789999194443226,
0.1465567561548079,
1.2142852250404639,
-1.0358380725229281,
-0.01806039941107697,
0.7775173290968913,
0.1473984532134141,
0.056367664585025117
], dtype=np.float32)
ACTION_STD = np.array([
0.009659401610150165,
5.529512395654432e-05,
7.415110423889358e-08,
0.0001335096561950502,
0.001149983840094768,
6.045416662412333e-05,
2.8176085110043344e-05,
0.28605545404572474,
0.8210758013481333,
0.6011500108903852,
0.0947890849009308,
0.2400083578393412,
0.1210455335794461,
0.033073617445561386
], 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)