Deploying learning-based controllers across heterogeneous robots is challenging due to platform differences, inconsistent interfaces, and inefficient middleware. To address these issues, we present UniCon, a lightweight framework that standardizes states, control flow, and instrumentation across platforms. It decomposes workflows into execution graphs with reusable components, separating system states from control logic to enable plug-and-play deployment across various robot morphologies. Unlike traditional middleware, it prioritizes efficiency through batched, vectorized data flow, minimizing communication overhead and improving inference latency. This modular, data-oriented approach enables seamless sim-to-real transfer with minimal re-engineering. We demonstrate that UniCon reduces code redundancy when transferring workflows and achieves higher inference efficiency compared to ROS-based systems. Deployed on over 12 robot models from 7 manufacturers, it has been successfully integrated into ongoing research projects, proving its effectiveness in real-world scenarios.
@article{arxiv.2601.14617,
title = {UniCon: A Unified System for Efficient Robot Learning Transfers},
author = {Yunfeng Lin and Li Xu and Yong Yu and Jiangmiao Pang and Weinan Zhang},
journal= {arXiv preprint arXiv:2601.14617},
year = {2026}
}
Comments
The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {10.1007/s11704-026-52064-1}