A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM

TL;DR

This paper introduces DP-VLA, a hierarchical dual-process framework that combines a slow, reasoning-based model with a fast, control-oriented model to enable efficient, real-time robotic manipulation using vision-language models.

Contribution

The paper presents a novel dual-process hierarchical framework for robotic manipulation that significantly improves efficiency and scalability by combining reasoning and control models.

Findings

Faster inference times compared to existing models

Higher task success rates on RoboCasa dataset

Scalable approach for real-time robotic tasks

Abstract

Vision-Language-Action (VLA) models are receiving increasing attention for their ability to enable robots to perform complex tasks by integrating visual context with linguistic commands. However, achieving efficient real-time performance remains challenging due to the high computational demands of existing models. To overcome this, we propose Dual Process VLA (DP-VLA), a hierarchical framework inspired by dual-process theory. DP-VLA utilizes a Large System 2 Model (L-Sys2) for complex reasoning and decision-making, while a Small System 1 Model (S-Sys1) handles real-time motor control and sensory processing. By leveraging Vision-Language Models (VLMs), the L-Sys2 operates at low frequencies, reducing computational overhead, while the S-Sys1 ensures fast and accurate task execution. Experimental results on the RoboCasa dataset demonstrate that DP-VLA achieves faster inference and higher task success rates, providing a scalable solution for advanced robotic applications.