--- 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](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
Trade-off Effect Mixture of Horizons
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](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} } ```