A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM

• URL: http://arxiv.org/abs/2410.15549v1
• Date: Mon, 21 Oct 2024 00:36:02 GMT
• Title: A Dual Process VLA: Efficient Robotic Manipulation Leveraging VLM
• Authors: ByungOk Han, Jaehong Kim, Jinhyeok Jang,
• Abstract summary: Vision-Language-Action (VLA) models enable robots to perform complex tasks by integrating visual context with linguistic commands. To overcome this, we propose Dual Process VLA (DP-VLA), a hierarchical framework inspired by dual-process theory. Experimental results on the RoboCasa dataset demonstrate that DP-VLA achieves faster inference and higher task success rates.
• Score: 0.26334346517416873
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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