LocoFormer - Generalist Locomotion via Long-Context Adaptation
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.
This open sourced work is sponsored by Safe Sentinel
@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}
}@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}
}@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},
}