mult-skill-act-models / normalization /all_points_v30_norm.py

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"""
归一化常量 - all_points_v30
从 data/all_points_v30/meta/stats.json 自动生成
"""
import numpy as np

# ====== 归一化统计量（从 all_points_v30 数据集提取）======

# State 归一化参数 (observation.state) - 14D
STATE_MEAN = np.array([
    -0.11861654811687596,
    0.6551543679232064,
    -0.6202130129977927,
    0.03739139159073152,
    0.718462864248691,
    -0.04730725484078222,
    0.03176325427660038,
    0.13648643951516923,
    0.5424953842266704,
    -0.5184459709995513,
    0.0127530837705511,
    0.651459814441072,
    0.007607655731635832,
    0.02749329034903046
], dtype=np.float32)

STATE_STD = np.array([
    0.20794573789805643,
    0.9042372547987999,
    0.6400128518438357,
    0.05858114586310527,
    0.1931048469288032,
    0.10647381729471782,
    0.03777411543902846,
    0.23687947334305431,
    0.8318566829034795,
    0.5613097073366347,
    0.07361109457742984,
    0.1648534650628715,
    0.10074111426319247,
    0.03291905705576544
], dtype=np.float32)

# Action 反归一化参数 - 14D (left_arm 7D + right_arm 7D)
ACTION_MEAN = np.array([
    -0.1188304002295586,
    0.6483761898240684,
    -0.6474010833429031,
    0.03750932871067081,
    0.6394424671797119,
    -0.050892829081253206,
    0.023357096419092584,
    0.13683727138887405,
    0.5352027132658975,
    -0.5383763909452893,
    0.014638734388523318,
    0.5783724995353671,
    0.006383864027813628,
    0.019046839673281905
], dtype=np.float32)

ACTION_STD = np.array([
    0.20945760549778225,
    0.8878992023943162,
    0.6424608192077861,
    0.06522725748321957,
    0.2225604872295908,
    0.10863453505390479,
    0.046969816666215136,
    0.23833706402423593,
    0.8178020058181515,
    0.5633031667143213,
    0.08081920744512357,
    0.18866411427203375,
    0.10249069113915953,
    0.04559958986517786
], 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)

	"""
	归一化常量 - all_points_v30
	从 data/all_points_v30/meta/stats.json 自动生成
	"""
	import numpy as np

	# ====== 归一化统计量（从 all_points_v30 数据集提取）======

	# State 归一化参数 (observation.state) - 14D
	STATE_MEAN = np.array([
	-0.11861654811687596,
	0.6551543679232064,
	-0.6202130129977927,
	0.03739139159073152,
	0.718462864248691,
	-0.04730725484078222,
	0.03176325427660038,
	0.13648643951516923,
	0.5424953842266704,
	-0.5184459709995513,
	0.0127530837705511,
	0.651459814441072,
	0.007607655731635832,
	0.02749329034903046
	], dtype=np.float32)

	STATE_STD = np.array([
	0.20794573789805643,
	0.9042372547987999,
	0.6400128518438357,
	0.05858114586310527,
	0.1931048469288032,
	0.10647381729471782,
	0.03777411543902846,
	0.23687947334305431,
	0.8318566829034795,
	0.5613097073366347,
	0.07361109457742984,
	0.1648534650628715,
	0.10074111426319247,
	0.03291905705576544
	], dtype=np.float32)

	# Action 反归一化参数 - 14D (left_arm 7D + right_arm 7D)
	ACTION_MEAN = np.array([
	-0.1188304002295586,
	0.6483761898240684,
	-0.6474010833429031,
	0.03750932871067081,
	0.6394424671797119,
	-0.050892829081253206,
	0.023357096419092584,
	0.13683727138887405,
	0.5352027132658975,
	-0.5383763909452893,
	0.014638734388523318,
	0.5783724995353671,
	0.006383864027813628,
	0.019046839673281905
	], dtype=np.float32)

	ACTION_STD = np.array([
	0.20945760549778225,
	0.8878992023943162,
	0.6424608192077861,
	0.06522725748321957,
	0.2225604872295908,
	0.10863453505390479,
	0.046969816666215136,
	0.23833706402423593,
	0.8178020058181515,
	0.5633031667143213,
	0.08081920744512357,
	0.18866411427203375,
	0.10249069113915953,
	0.04559958986517786
	], 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)