LocoFormer (wip)
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.
Install
$ pip install locoformer
Usage
import torch
from locoformer.locoformer import Locoformer
# mock robot embodied with some state dimensions and action dimensions
locoformer = Locoformer(
embedder = dict(
dim = 512,
dim_state = [32, 16], # support multiple bodies / robots
),
unembedder = dict(
num_continuous = 12 + 6,
selectors = [
list(range(12)),
list(range(12, 12 + 6))
]
),
transformer = dict(
dim = 512,
depth = 6,
heads = 8,
window_size = 32
)
)
# mock state from one of the robots (0th one)
state = torch.randn(1, 1, 32)
# forward to get action logits
action_logits, _ = locoformer(
state,
state_embed_kwargs = dict(state_type = 'raw'),
state_id_kwarg = dict(state_id = 0),
action_select_kwargs = dict(selector_index = 0)
)
# sample action using the internal distribution
action = locoformer.unembedder.sample(action_logits, selector_index = 0) # (1, 1, 12)
Sponsors
This open sourced work is sponsored by Safe Sentinel
Citations
@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},
}
@misc{osband2026delightfulpolicygradient,
title = {Delightful Policy Gradient},
author = {Ian Osband},
year = {2026},
eprint = {2603.14608},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2603.14608},
}
@misc{schwarzer2021dataefficientreinforcementlearningselfpredictive,
title = {Data-Efficient Reinforcement Learning with Self-Predictive Representations},
author = {Max Schwarzer and Ankesh Anand and Rishab Goel and R Devon Hjelm and Aaron Courville and Philip Bachman},
year = {2021},
eprint = {2007.05929},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2007.05929},
}
@misc{teoh2026nextlatentpredictiontransformerslearn,
title = {Next-Latent Prediction Transformers Learn Compact World Models},
author = {Jayden Teoh and Manan Tomar and Kwangjun Ahn and Edward S. Hu and Tim Pearce and Pratyusha Sharma and Akshay Krishnamurthy and Riashat Islam and Alex Lamb and John Langford},
year = {2026},
eprint = {2511.05963},
archivePrefix = {arXiv},
primaryClass = {cs.LG},
url = {https://arxiv.org/abs/2511.05963},
}
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