让四足机器人更懂人话指令，实现稳定连续控制。
MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots
- 构建多粒度思维链数据集，指导机器人理解语言指令
- 两阶段训练使推理与动作更一致，长程执行提升5%
- 实机测试在复杂环境表现可靠，适合移动机器人研究
将自然语言指令落地为四足机器人的连续控制仍是视觉-语言-动作领域的核心挑战。现有方法难以衔接高层语义推理与底层执行，导致现实场景中存在接地不稳、泛化能力弱的问题。为此，我们提出MobileVLA-R1，一个统一的视觉-语言-动作框架，支持四足机器人的显式推理与连续控制。我们构建了MobileVLA-CoT——一个大规模多粒度思维链（CoT）数据集，为具身轨迹提供结构化推理监督。基于此，采用两阶段训练：先进行有监督的CoT对齐，再通过GRPO强化学习优化推理一致性、控制稳定性与长时序执行能力。在VLN和VLA任务上的大量评估显示，性能优于强基线约5%。真实四足机器人部署验证了其在复杂环境中的鲁棒表现。代码与主页见：https://github.com/AIGeeksGroup/MobileVLA-R1, https://aigeeksgroup.github.io/MobileVLA-R1。
原文摘要 · Abstract (English)
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.
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