https://github.com/lucidrains/locoformer
LocoFormer - Generalist Locomotion via Long-Context Adaptation
https://github.com/lucidrains/locoformer
adaptation artificial-intelligence attention-mechanisms cross-embodiment deep-learning locomotion transformers
Last synced: 5 months ago
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LocoFormer - Generalist Locomotion via Long-Context Adaptation
- Host: GitHub
- URL: https://github.com/lucidrains/locoformer
- Owner: lucidrains
- License: mit
- Created: 2025-09-25T13:02:18.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2026-01-22T15:05:19.000Z (5 months ago)
- Last Synced: 2026-01-23T09:57:34.351Z (5 months ago)
- Topics: adaptation, artificial-intelligence, attention-mechanisms, cross-embodiment, deep-learning, locomotion, transformers
- Language: Python
- Homepage:
- Size: 34.5 MB
- Stars: 97
- Watchers: 0
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 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) and extreme domain randomization. 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}
}
```
```bibtex
@inproceedings{anonymous2025flow,
title = {Flow Policy Gradients for Legged Robots},
author = {Anonymous},
booktitle = {Submitted to The Fourteenth International Conference on Learning Representations},
year = {2025},
url = {https://openreview.net/forum?id=BA6n0nmagi},
note = {under review}
}
```
```bibtex
@misc{ashlag2025stateentropyregularizationrobust,
title = {State Entropy Regularization for Robust Reinforcement Learning},
author = {Yonatan Ashlag and Uri Koren and Mirco Mutti and Esther Derman and Pierre-Luc Bacon and Shie Mannor},
year = {2025},
eprint = {2506.07085},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2506.07085},
}
```