Multi-Agent Robotic Control with Onboard Vision-Language Models

2026-07-09 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning, Manufacturing & Industrial · Depth: Expert, long

Summary

A Multi-Agent System (MAS) architecture is presented for robotic control using onboard Vision-Language Models (VLMs), addressing challenges like explainability, generalization, and high compute requirements. The system controls a multi-purpose autonomous mobile manipulator in a simulated industrial warehouse, performing five task categories: safety inspection, warehouse maintenance, search, package quality verification, and human request response. It deploys specialized agents on onboard hardware, specifically an AMD Ryzen™ AI mini PC, eliminating cloud dependence. Compact VLMs (3-20B parameters) are utilized, with the LFM2-VL-3B model fine-tuned to improve package inspection accuracy from 76.7% to 91.5% F1-score. A "Megamind" orchestration agent manages long-horizon planning and context retention for smaller models. Validated in a hardware-in-the-loop simulation, the system demonstrates a viable, cost-efficient alternative to cloud deployments. The simulation environment is open source under the Apache 2.0 license.

Key takeaway

For Machine Learning Engineers developing robotic control systems, this research demonstrates that you can achieve robust, real-time performance without cloud dependence. You should consider a multi-agent architecture with compact, fine-tuned VLMs running on onboard hardware like an AMD Ryzen™ AI mini PC. This approach mitigates context issues in long-horizon planning and offers a cost-efficient, explainable alternative to larger, cloud-based models, justifying real-robot trials for your flexible robotics applications.

Key insights

Onboard multi-agent systems with compact, fine-tuned VLMs offer a cost-efficient, explainable, and generalizable solution for robotic control.

Principles

• Deploy specialized agents on onboard hardware.
• Fine-tune compact VLMs for task-specific accuracy.
• Orchestrate agents to manage context for long-horizon tasks.

Method

A multi-agent system (MAS) uses a supervisory "Megamind" agent for task delegation and recovery, coordinating specialized agents (Inspection, Safety, MoveIt, Nav2) that utilize a fine-tuned 3B-parameter VLM for real-time, onboard robotic control.

In practice

• Implement a "Megamind" agent for task orchestration.
• Fine-tune compact VLMs on synthetic data for specific tasks.
• Integrate ROS 2, MoveIt 2, and Nav2 for low-level control.

Topics

• Multi-Agent Systems
• Onboard VLMs
• Robotic Control
• Industrial Automation
• Hardware-in-the-Loop
• AMD Ryzen AI

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Robotics Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.