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.

翻译：由于平台差异、接口不一致以及中间件效率低下，基于学习的控制器在异构机器人间的部署面临挑战。为解决这些问题，我们提出了UniCon，一个轻量级框架，用于标准化不同平台间的状态、控制流与仪器化。它将工作流分解为包含可复用组件的执行图，将系统状态与控制逻辑分离，从而实现跨多种机器人形态的即插即用部署。与传统中间件不同，UniCon通过批量化、向量化的数据流优先考虑效率，最小化通信开销并改善推理延迟。这种模块化、面向数据的方法能够以最少的重新工程实现无缝的仿真到现实迁移。我们证明，与基于ROS的系统相比，UniCon在迁移工作流时减少了代码冗余，并实现了更高的推理效率。该框架已在来自7家制造商的超过12种机器人模型上部署，并成功集成到多个进行中的研究项目中，证明了其在真实场景中的有效性。