Model Card for Model ID
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]
TODO
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-RVQft", 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}")
Downstream Use [optional]
TODO
Out-of-Scope Use
TODO
Bias, Risks, and Limitations
TODO
Recommendations
TODO
How to Get Started with the Model
Use the code below to get started with the model.
TODO
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]