Towards Accessible Physical AI: LoRA-Based Fine-Tuning of VLA Models for Real-World Robot Control

TL;DR

This paper introduces a resource-efficient fine-tuning approach using LoRA and quantization to adapt large VLA models for real-world robot control on affordable hardware, enabling advanced manipulation tasks with limited data.

Contribution

It presents a novel LoRA-based fine-tuning method for large VLA models, making them deployable on low-cost robots with minimal data and computational resources.

Findings

Effective manipulation achieved on low-cost robotic arm

Fine-tuning with limited data improves real-world performance

Models run on consumer-grade GPUs with 8GB VRAM

Abstract

Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in robotic manipulation,enabling robots to execute natural language commands through end-to-end learning from visual observations.However, deploying large-scale VLA models on affordable robotic platforms remains challenging due to computational constraints and the need for efficient adaptation to new robot embodiments. This paper presents an efficient fine-tuning methodology and real-world deployment analysis for adapting VLA models to low-cost robotic manipulation systems.We propose a resource-efficient fine-tuning strategy using Low-Rank Adaptation (LoRA) and quantization techniques that enable multi-billion parameter VLA models ( 3.1B parameters) to run on consumer-grade GPUs with 8GB VRAM. Our methodology addresses the critical challenge of adapting pre-trained VLA models to new robot embodiments with limited demonstration data, focusing on the trade-offs between frozen and unfrozen vision encoders. Through real-world deployment on the SO101 robotic arm for a button-pressing manipulation task, we demonstrate that our approach achieves effective manipulation performance while maintaining computational efficiency. We provide detailed analysis of deployment challenges, failure modes, and the relationship between training data quantity and real-world performance,trained on 200 demonstration episodes. Our results show that with proper fine-tuning methodology, VLA models can be successfully deployed on affordable robotic platforms,making advanced manipulation capabilities accessible beyond expensive research robots.