File size: 3,692 Bytes
5d2a962 26223d4 5d2a962 7dd566c 5d2a962 7dd566c 26223d4 5d2a962 26223d4 2601235 26223d4 5d2a962 26223d4 5d2a962 26223d4 5d2a962 26223d4 5d2a962 26223d4 5d2a962 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
pipeline_tag: robotics
license: apache-2.0
tags:
- reinforcement-learning
- robotic-manipulation
- action-chunking
---
# This repository provided the torch-version openpi checkpoints (pi0base, pi0.5base) coverted from JAX-version!
# [Mixture of Horizons in Action Chunking](https://huggingface.co/papers/2511.19433)
Vision-language-action (VLA) models for robotic manipulation are highly sensitive to the chosen **action chunk length**, termed **horizon** in this work. A fixed horizon presents an inherent trade-off: longer horizons offer superior global foresight but compromise fine-grained accuracy, while shorter ones provide precise local control but struggle with long-term tasks.
To address this challenge, we propose **Mixture of Horizons (MoH)**, a novel, plug-and-play strategy that fuses multiple horizons within a single policy. MoH processes action chunks in parallel segments with different horizons and integrates their outputs. This approach simultaneously leverages long-term foresight and short-term precision with minimal overhead, and enables **Dynamic Inference** through cross-horizon consensus for enhanced efficiency and robustness in complex robotic tasks.
- 📄 [Paper](https://huggingface.co/papers/2511.19433)
- 📝 [Project Page](https://timsty1.github.io/moh/)
- 💻 [Code](https://github.com/Timsty1/MixtureOfHorizons/tree/main)
## Introduction
<div align="center">
<table border="0" cellspacing="0" cellpadding="0">
<tr>
<td align="center" width="50%">
<img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/study_of_horizons_pi0.png" alt="Trade-off Effect" width="100%">
</td>
<td align="center" width="50%">
<img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/intro_motivation_v2.png" alt="Mixture of Horizons" width="100%">
</td>
</tr>
<tr>
<td align="center" valign="top">
Figure 1: Trade-off between long-term foresight and short-term precision induced by single horizon
</td>
<td align="center" valign="top">
Figure 2: Overview of the proposed mixture-of-horizons strategy
</td>
</tr>
</table>
</div>
<br>
* **Mitigates Trade-off**: Addresses the inherent trade-off between long-term foresight and short-term precision induced by single action chunk horizons.
* **Plug-and-Play**: Easily integrates into existing full-attention action modules with minimal training or inference overhead.
* **Dynamic Inference**: Achieves higher efficiency and robustness by selecting stable actions through cross-horizon consensus.
#### More results on LIBERO
<div align="center">
<img src="https://github.com/Timsty1/MixtureOfHorizons/raw/main/figure/libero_main.jpg" width="90%" />
</div>
## Usage
For detailed instructions on environment setup, training, and evaluation, please refer to the [GitHub repository](https://github.com/Timsty1/MixtureOfHorizons/tree/main).
## ❤️ Acknowledgment
We express our gratitude to [OpenPi](https://github.com/Physical-Intelligence/openpi/tree/main), [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO), and [RoboTwin](https://robotwin-platform.github.io/) for their open-source contributions.
## 📝 Citation
If you feel that this paper, models, or codes are helpful, please cite our paper, thanks for your support!
```bibtex
@article{jing2025mixture_of_horizons,
title={Mixture of Horizons in Action Chunking},
author={Jing, Dong and Wang, Gang and Liu, Jiaqi and Tang, Weiliang and Sun, Zelong and Yao, Yunchao and Wei, Zhenyu and Liu, Yunhui and Lu, Zhiwu and Ding, Mingyu},
journal={arXiv preprint arXiv:2511.19433},
year={2025}
}
``` |