MoIRA: Modular Instruction Routing Architecture for Multi-Task Robotics
Dmytro Kuzmenko, Nadiya Shvai

TL;DR
MoIRA introduces a modular, text-based routing framework for multi-task robotics that efficiently coordinates multiple experts, improving task performance and robustness without extensive internal reconfiguration.
Contribution
MoIRA presents a novel architecture-agnostic, modular MoE framework with external text-based routing, enabling flexible expert coordination and zero-shot routing options in robotics.
Findings
Outperforms generalist models on multiple benchmarks.
Demonstrates robustness to instruction variations.
Provides scalable, low-effort expert deployment.
Abstract
Mixture-of-Experts (MoE) approaches have recently gained traction in robotics applications due to their ability to dynamically allocate computational resources and specialize sub-networks for distinct tasks or environmental contexts, enabling more efficient decision-making. Such systems often comprise sparsely activated experts combined under a single monolithic architecture and require a well-configured internal routing mechanism, which does not allow for selective low-level expert and router customization and requires additional training. We propose MoIRA, an architecture-agnostic modular MoE framework designed to coordinate existing experts with an external text-based router. MoIRA incorporates two zero-shot routing options: embedding-based similarity and prompt-driven language model inference. In our experiments, we choose large Vision-Language-Action models, gr00t-N1 and ,…
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