# UniCon: A Unified System for Efficient Robot Learning Transfers

arXiv: https://arxiv.org/abs/2601.14617 · HTML: https://mlsanity.com/paper/2601.14617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Embodiment-Aware Generalist Specialist Distillation for Unified Humanoid Whole-Body Control](https://mlsanity.com/paper/2602.02960/) (2026)

## Abstract (arXiv)

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.