pipeline_tag: robotics
license: apache-2.0
tags:
- reinforcement-learning
- robotic-manipulation
- action-chunking
Mixture of Horizons in Action Chunking
This repository hosts the official implementation of Mixture of Horizons (MoH), introduced in the paper Mixture of Horizons in Action Chunking.
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
- 📝 Project Page
- 💻 Code
Introduction
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| Figure 1: Trade-off between long-term foresight and short-term precision induced by single horizon | Figure 2: Overview of the proposed mixture-of-horizons strategy |
- 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
Usage
For detailed instructions on environment setup, training, and evaluation, please refer to the GitHub repository.
❤️ Acknowledgment
We express our gratitude to OpenPi, LIBERO, and RoboTwin 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!
@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}
}