MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots

TL;DR

MobileVLA-R1 introduces a unified framework for grounding natural language instructions into continuous control for quadruped robots, combining structured reasoning and reinforcement learning to improve stability and generalization in real-world tasks.

Contribution

The paper presents MobileVLA-R1, a novel vision-language-action framework with a large-scale reasoning dataset and a two-stage training paradigm, advancing the integration of reasoning and control in mobile robots.

Findings

Achieved approximately 5% performance improvement over baselines.

Demonstrated robust real-world deployment on quadruped robots.

Enhanced reasoning consistency and control stability in complex environments.

Abstract

Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge high-level semantic reasoning and low-level actuation, leading to unstable grounding and weak generalization in the real world. To address these issues, we present MobileVLA-R1, a unified vision-language-action framework that enables explicit reasoning and continuous control for quadruped robots. We construct MobileVLA-CoT, a large-scale dataset of multi-granularity chain-of-thought (CoT) for embodied trajectories, providing structured reasoning supervision for alignment. Built upon this foundation, we introduce a two-stage training paradigm that combines supervised CoT alignment with GRPO reinforcement learning to enhance reasoning consistency, control stability, and long-horizon execution. Extensive evaluations on VLN and VLA tasks demonstrate superior performance over strong baselines, with approximately a 5% improvement. Real-world deployment on a quadruped robot validates robust performance in complex environments. Code: https://github.com/AIGeeksGroup/MobileVLA-R1. Website: https://aigeeksgroup.github.io/MobileVLA-R1.