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

Grounding natural-language instructions into executable con-tinuous control remains a core challenge for quadruped vision-language-action (VLA) systems due to the gap between high-level semantic reason-ing and low-level locomotion actuation. Existing approaches often relyon implicit reasoning or purely behavioral supervision, leading to unsta-ble long-horizon grounding and limited robustness in real-world deploy-ment. To address these issues, we present MobileVLA-R1, a unifiedquadruped VLA framework that explicitly aligns hierarchical reason-ing with continuous control. Our method introduces a multi-granularitychain-of-thought (CoT) supervision scheme for embodied trajectories, in-stantiated in MobileVLA-CoT, together with a two-stage training paradigmthat combines supervised CoT alignment and reinforcement learning forreasoning-to-control consistency. We conduct comprehensive evaluationson VLN and quadruped VLA benchmarks, including VLN-CE, QUARD,and real-world Unitree Go2 deployment. MobileVLA-R1 consistently out-performs strong baselines, achieving about 5% gains on key metrics whiledemonstrating deployment-time closed-loop execution under a fixed hy-brid inference setup.