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Model Details
Model Description
ActionCodec model trained on 3 embodiments:
- franka_libero_20hz_1s
- widowx_bridge_5hz_3s
- franka_droid_15hz_1s
Model Sources [optional]
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Uses
Direct Use
import numpy as np
from transformers import AutoModel
np.set_printoptions(suppress=True)
if __name__ == "__main__":
tokenizer = AutoModel.from_pretrained("ZibinDong/ActionCodec-Base", trust_remote_code=True)
q99 = np.array([0.9375, 0.91071427, 0.9375, 0.20357142, 0.26357144, 0.375, 1.0])
q01 = np.array([-0.87857145, -0.87589288, -0.9375, -0.15107143, -0.20678571, -0.27964285, 0.0])
# an example action from physical-intelligence/libero
action = np.array(
[
[0.3268, 0.2089, -0.3295, 0.0000, -0.0868, -0.0611, 1.0000],
[0.3696, 0.1955, -0.2866, 0.0000, -0.0793, -0.0643, 1.0000],
[0.3857, 0.1929, -0.2759, 0.0000, -0.0782, -0.0654, 1.0000],
[0.3964, 0.2089, -0.2786, 0.0000, -0.0761, -0.0654, 1.0000],
[0.3321, 0.1741, -0.3268, 0.0000, -0.0793, -0.0686, 1.0000],
[0.2250, 0.0964, -0.4232, 0.0000, -0.0932, -0.0761, 1.0000],
[0.0723, 0.0000, -0.5625, 0.0000, -0.1339, -0.0879, 1.0000],
[0.0536, 0.0000, -0.5652, 0.0000, -0.1521, -0.0921, 1.0000],
[0.0750, 0.0000, -0.5464, 0.0000, -0.1511, -0.0964, 1.0000],
[0.0723, 0.0000, -0.5411, 0.0000, -0.1414, -0.0986, 1.0000],
[0.0402, 0.0000, -0.5196, 0.0000, -0.1350, -0.1007, 1.0000],
[0.0080, 0.0000, -0.4795, 0.0000, -0.1189, -0.1018, 1.0000],
[0.0000, 0.0000, -0.4527, 0.0000, -0.0986, -0.1018, 1.0000],
[0.0000, 0.0000, -0.4313, 0.0000, -0.0846, -0.1018, 1.0000],
[-0.0455, -0.0268, -0.3509, 0.0000, -0.0568, -0.1018, 1.0000],
[-0.0964, -0.0482, -0.3321, 0.0000, -0.0439, -0.1039, 1.0000],
[-0.1768, -0.0562, -0.3402, 0.0000, -0.0300, -0.1050, 1.0000],
[-0.2438, -0.0429, -0.3187, 0.0000, -0.0193, -0.0996, 1.0000],
[-0.3054, -0.0054, -0.2893, 0.0000, -0.0139, -0.0932, 1.0000],
[-0.3509, 0.0000, -0.2598, 0.0000, -0.0054, -0.0879, 1.0000],
],
)[None]
# normalization
normalized_action = np.copy(action)
normalized_action[..., :-1] = normalized_action[..., :-1] / np.maximum(np.abs(q99), np.abs(q01))[..., :-1]
normalized_action[..., -1] = normalized_action[..., -1] * 2.0 - 1.0 # scale to [-1, 1]
normalized_action = normalized_action.clip(-1.0, 1.0)
# tokenization
tokens = tokenizer.encode(normalized_action) # numpy (b, n, d) -> list of ints
print(tokens)
# decoding
decoded_action, padding_mask = tokenizer.decode(tokens) # list of ints -> numpy (b, n, d)
# calculate reconstruction error
mse_error = np.mean((normalized_action - decoded_action) ** 2)
l1_error = np.mean(np.abs(normalized_action - decoded_action))
print(f"Reconstruction MSE error: {mse_error:.6f}")
print(f"Reconstruction L1 error: {l1_error:.6f}")
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Training Details
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