UniCon: A Unified System for Efficient Robot Learning Transfers

TL;DR

UniCon is a lightweight, modular framework that standardizes robot control workflows to enable efficient, plug-and-play transfer of learning-based controllers across diverse robot platforms.

Contribution

It introduces a unified, data-oriented system that reduces code redundancy and improves inference efficiency for robot learning transfers.

Findings

UniCon reduces code redundancy across robot platforms.

It achieves higher inference efficiency than traditional ROS systems.

Successfully deployed on over 12 robot models from 7 manufacturers.

Abstract

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.