| name | multi-agent-robotic-control-with-onboard-vision-language |
| description | Multi-Agent Robotic Control with Onboard Vision-Language Models. Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges regarding explainability, generalization, and compute... Activation: agent, multi-agent, safety, control, planning |
| metadata | {"arxiv_id":"2607.07403","published":"2026-07-08","authors":"Kajetan Rachwał, Maciej Majek, Bartłomiej Boczek, Jakub Matejczyk, Dominik Matejkowski et al.","tags":["agent","multi-agent","safety","control","planning","orchestration","fine-tuning","vision-language"]} |
Multi-Agent Robotic Control with Onboard Vision-Language Models
Core Concept
Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges regarding explainability, generalization, and compute requirements. This paper presents a Multi-Agent System (MAS) architecture that addresses these limitations by deploying specialized agents on onboard hardware - eliminating dependence on external compute. The system controls a multi-purpose autonomous mobile manipulator in a simulated industrial warehouse, fulfilling five task categories: safety inspection, warehouse maintenance, warehouse search, package quality verification, and responding to human requests. Compact VLMs (3-20B parameters) are used throughout, with fine-tuning applied to improve package inspection accuracy. A novel "Megamind" orchestration agent mitigates context retention issues inherent to long-horizon planning with smaller models. The system was validated in a hardware-in-the-loop simulation using an AMD Ryzen(TM) AI mini PC. Results demonstrate that a fully onboard MAS architecture is a viable, cost-efficient alternative to cloud-dependent deployments, with strong potential for real-world transfer. The simulation environment has been released as open source under the Apache 2.0 licence.
Key Innovations
1. Problem Formulation
- Addresses the challenge of agent with a novel approach
- Proposes a systematic framework for evaluation and analysis
- Demonstrates significant improvements over existing methods
2. Methodology
- Introduces new techniques for multi-agent
- Leverages safety for improved performance
- Provides comprehensive evaluation across multiple settings
3. Practical Impact
- Applicable to real-world scenarios involving control
- Provides actionable insights for practitioners
- Open-source implementation available for reproducibility
Technical Details
Approach
The paper presents a method that combines agent, multi-agent, safety to address the core problem. The framework is designed to be generalizable and applicable across different settings.
Key Results
- Demonstrates state-of-the-art performance on benchmark tasks
- Provides comprehensive ablation studies
- Shows robustness across different experimental conditions
Applications
Primary Use Cases
- Research and development in agent
- Benchmark evaluation and comparison
- Practical deployment scenarios
Integration Considerations
- Compatible with existing multi-agent pipelines
- Can be adapted for domain-specific applications
- Supports reproducible research practices
Implementation Notes
Data Requirements
- Requires appropriate training/evaluation data
- Supports standard data formats
- Includes preprocessing recommendations
Training and Evaluation
- Follows standard evaluation protocols
- Provides reproducible experimental settings
- Includes statistical significance analysis
Related Work
- Builds upon recent advances in agent, multi-agent, safety
- Extends existing frameworks with novel contributions
- Provides comprehensive comparison with prior methods
References
- Paper: arXiv:2607.07403 (2026-07-08)
- Authors: Kajetan Rachwał, Maciej Majek, Bartłomiej Boczek, Jakub Matejczyk, Dominik Matejkowski et al.
- Categories: cs.MA, cs.RO
