Metadata-Version: 2.4
Name: locoformer
Version: 0.0.6
Summary: LocoFormer
Project-URL: Homepage, https://pypi.org/project/locoformer/
Project-URL: Repository, https://github.com/lucidrains/locoformer
Author-email: Phil Wang <lucidrains@gmail.com>
License: MIT License
        
        Copyright (c) 2025 Phil Wang
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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License-File: LICENSE
Keywords: artificial intelligence,attention mechanism,cross-embodiment,deep learning,robotics,transformer
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Requires-Dist: assoc-scan
Requires-Dist: einops>=0.8.0
Requires-Dist: einx>=0.3.0
Requires-Dist: rotary-embedding-torch
Requires-Dist: torch>=2.4
Requires-Dist: x-mlps-pytorch
Provides-Extra: examples
Requires-Dist: accelerate; extra == 'examples'
Requires-Dist: tqdm; extra == 'examples'
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Description-Content-Type: text/markdown

<img src="./fig3.png" width="400px"></img>

## LocoFormer (wip)

[LocoFormer - Generalist Locomotion via Long-Context Adaptation](https://generalist-locomotion.github.io/)

The gist is they trained a simple Transformer-XL in simulation on robots with many different bodies (cross-embodiment). When transferring to the real-world, they noticed the robot now gains the ability to adapt to insults. The XL memories span across multiple trials, which allowed the robot to learn in-context adaptation.

## Sponsors

This open sourced work is sponsored by [Safe Sentinel](https://www.safesentinels.com/)

## Citations

```bibtex
@article{liu2025locoformer,
    title   = {LocoFormer: Generalist Locomotion via Long-Context Adaptation},
    author  = {Liu, Min and Pathak, Deepak and Agarwal, Ananye},
    journal = {Conference on Robot Learning ({CoRL})},
    year    = {2025}
}
```
