UniCon: A Unified System for Efficient Robot Learning Transfers
This work addresses the problem of inefficient robot learning transfers for researchers and developers, but it is incremental as it builds on existing middleware concepts with a focus on efficiency and modularity.
The authors tackled the challenge of deploying learning-based controllers across heterogeneous robots by introducing UniCon, a lightweight framework that standardizes states and control flow, which reduces code redundancy and achieves higher inference efficiency compared to ROS-based systems.
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.