Modular Diffusion Policies Boost Multitask Robot Learning, Study Finds

Global: Modular Diffusion Policies Boost Multitask Robot Learning, Study Finds

A team of researchers from multiple institutions announced a new approach to multitask robot learning that leverages a modular diffusion policy framework, according to a paper posted on arXiv on Dec 26, 2025. The method factorizes complex action distributions into specialized diffusion components, aiming to improve policy flexibility and reduce underfitting.

Challenges with Monolithic Policies

The authors explain that traditional monolithic models often struggle with the highly multimodal nature of robot actions, leading to performance gaps when handling diverse tasks. By decomposing the behavior space, each diffusion module captures a distinct sub‑mode, enabling more accurate representation of the overall policy.

Factorized Diffusion Architecture

In addition to performance gains, the modular architecture is designed to facilitate adaptation to new tasks. The paper notes that new components can be added or existing ones fine‑tuned without overwriting previously learned behaviors, thereby mitigating catastrophic forgetting.

Adaptation and Forgetting

Empirical evaluation was conducted in both simulated environments and real‑world robotic manipulation scenarios. The authors report that their approach consistently outperformed strong modular and monolithic baselines across a range of benchmark tasks.

Scalability and Efficiency

The study also highlights the scalability of the factorized diffusion policy. Because each module operates independently, training can be parallelized, potentially reducing computational overhead compared with training a single large model.

Limitations and Future Work

Limitations mentioned include the need for careful selection of the number of diffusion modules and the potential for increased system complexity when managing many components. Future work outlined by the authors includes automated module discovery and integration with reinforcement learning pipelines.

The paper is listed under the Robotics (cs.RO) and Artificial Intelligence (cs.AI) categories on arXiv and carries the identifier arXiv:2512.21898. The authors include Chaoqi Liu, Haonan Chen, Sigmund H. Høeg, Shaoxiong Yao, Yunzhu Li, Kris Hauser, and Yilun Du.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.