统一机器人学习迁移框架，实现跨平台高效部署
UniCon: A Unified System for Efficient Robot Learning Transfers
- 将控制流程拆解为可复用组件，分离状态与逻辑
- 相比ROS系统，推理延迟更低，代码冗余减少
- 已适配12种机器人，适合需要快速移植的科研项目
在异构机器人上部署基于学习的控制器面临平台差异、接口不一致和低效中间件等挑战。为此，我们提出UniCon——一个轻量级框架，通过标准化状态、控制流和监测机制，实现跨平台一致性。该框架将工作流分解为可重用组件的执行图，分离系统状态与控制逻辑，支持即插即用的多形态机器人部署。与传统中间件不同，它采用批量向量化数据流设计，显著降低通信开销，提升推理效率。这种模块化、数据驱动的方法实现了无需大量重构的仿真到现实迁移。实验表明，UniCon能有效减少工作流迁移中的代码冗余，并在推理效率上优于基于ROS的系统。该框架已成功部署于7家厂商的12种机器人平台上，集成至多个在研项目中，验证了其在真实场景中的有效性。
原文摘要 · Abstract (English)
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.
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