VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

TL;DR

VILAS is a low-cost, modular robotic platform supporting vision-language-action policy learning, featuring a soft gripper for fragile object manipulation, and validated through grape grasping experiments.

Contribution

The paper introduces VILAS, a cost-effective robotic system with a soft gripper and unified communication, enabling end-to-end VLA policy deployment on accessible hardware.

Findings

State-of-the-art VLA models successfully deployed on VILAS.

Soft gripper enables safe manipulation of fragile objects.

Experimental validation on grape grasping task confirms system effectiveness.

Abstract

We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning and deployment on accessible hardware. The system integrates a Fairino FR5 collaborative arm, a Jodell RG52-50 electric gripper, and a dual-camera perception module, unified through a ZMQ-based communication architecture that seamlessly coordinates teleoperation, data collection, and policy deployment within a single framework. To enable safe manipulation of fragile objects without relying on explicit force sensing, we design a kirigami-based soft compliant gripper extension that induces predictable deformation under compressive loading, providing gentle and repeatable contact with delicate targets. We deploy and evaluate three state-of-the-art VLA models on the VILAS platform: pi_0, pi_0.5, and GR00T N1.6. All models are fine-tuned from publicly released pretrained checkpoints using an identical demonstration dataset collected via our teleoperation pipeline. Experiments on a grape grasping task validate the effectiveness of the proposed system, confirming that capable manipulation policies can be successfully trained and deployed on low-cost modular hardware. Our results further provide practical insights into the deployment characteristics of current VLA models in real-world settings.