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

• URL: http://arxiv.org/abs/2511.17889v1
• Date: Sat, 22 Nov 2025 02:34:10 GMT
• Title: MobileVLA-R1: Reinforcing Vision-Language-Action for Mobile Robots
• Abstract summary: MobileVLA-R1, a vision-language-action framework, enables explicit reasoning and continuous control for quadruped robots.<n>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.
• Score: 15.919707197385462
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control [63.85634404397772]
  MobileVLA-R1 2.0 is an RL-enhanced vision-language-action framework for mobile robots.<n>It learns multi-granularity reasoning over embodied trajectories through supervised Chain-of-Thought (CoT) alignment and reinforcement learning.<n>We conduct evaluations on language-guided navigation, quadruped control, and humanoid mobile manipulation, covering VLN-CE, QUARD, and real-world deployments on Unitree Go2 and G1 robots.
  arXiv  Detail & Related papers  (2026-09-05T20:31:14Z)
• OneVLA: A Unified Framework for Embodied Tasks [42.816376426057275]
  navigation and manipulation are fundamental capabilities of embodied intelligence.<n>OneVLA is a unified architecture that integrates these distinct tasks into a single, cohesive framework.<n>Experiments in both simulated and real-world environments demonstrate that OneVLA achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-05-31T13:43:23Z)
• DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA [72.9197085473598]
  DIAL is a framework bridging high-level decision making and low-level motor execution through a differentiable latent intent bottleneck.<n>A VLM-based System-2 performs latent world modeling by latent visual foresight within the VLM's native feature space.<n>A lightweight System-1 policy then decodes this predicted intent together with the current observation into precise robot actions.
  arXiv  Detail & Related papers  (2026-03-31T15:02:27Z)
• AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models [9.608633915316252]
  Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation.<n>Current paradigms rely on coarse, high-level task instructions during supervised fine-tuning.<n>We propose method, the first subtask-aware VLA framework integrated with a scalable offline post-training pipeline.
  arXiv  Detail & Related papers  (2026-03-09T15:52:48Z)
• OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL [63.388513841293616]
  Existing forgery detection methods fail to handle the interleaved text, images, and videos prevalent in real-world misinformation.<n>To bridge this gap, this paper targets to develop a unified framework for omnibus vision-language forgery detection and grounding.<n>We propose textbf OmniVL-Guard, a balanced reinforcement learning framework for omnibus vision-language forgery detection and grounding.
  arXiv  Detail & Related papers  (2026-02-11T09:41:36Z)
• Green-VLA: Staged Vision-Language-Action Model for Generalist Robots [0.0]
  We introduce Green-VLA, a staged Vision-Language-Action framework for real-world deployment on the Green humanoid robot.<n>We couple a scalable data-processing pipeline with temporal alignment and quality filtering, and use a unified, embodiment-aware action interface.<n>Experiments on Simpler BRIDGE WidowX and CALVIN ABC-D, as well as real-robot evaluations, show strong generalization and performance gains from RL alignment.
  arXiv  Detail & Related papers  (2026-01-31T22:13:23Z)
• REALM: A Real-to-Sim Validated Benchmark for Generalization in Robotic Manipulation [46.970355660265284]
  Vision-Language-Action (VLA) models empower robots to understand and execute tasks described by natural language instructions.<n>A key challenge lies in their ability to generalize beyond the specific environments and conditions they were trained on.<n>We present REALM, a new simulation environment and benchmark designed to evaluate the generalization capabilities of VLA models.
  arXiv  Detail & Related papers  (2025-12-22T16:44:23Z)
• Lite VLA: Efficient Vision-Language-Action Control on CPU-Bound Edge Robots [0.6119773373677944]
  This work demonstrates the feasibility of deploying small Vision-Language Models (VLMs) on mobile robots to achieve real-time scene understanding and reasoning under strict computational constraints.<n>Unlike prior approaches that separate perception from mobility, the proposed framework enables simultaneous movement and reasoning in dynamic environments using only on-board hardware.
  arXiv  Detail & Related papers  (2025-11-07T17:49:14Z)
• dVLA: Diffusion Vision-Language-Action Model with Multimodal Chain-of-Thought [66.78110237549087]
  Vision-Language-Action (VLA) models are emerging as a next-generation paradigm for robotics.<n>We introduce dVLA, a diffusion-based VLA that unifies visual perception, language reasoning, and robotic control in a single system.
  arXiv  Detail & Related papers  (2025-09-30T02:36:11Z)
• ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks [46.676862567167625]
  ODYSSEY is a unified mobile manipulation framework for agile quadruped robots equipped with manipulators.<n>To address the challenge of egocentric perception in language-conditioned tasks, we introduce a hierarchical planner powered by a vision-language model.<n>At the control level, our novel whole-body policy achieves robust coordination across challenging terrains.
  arXiv  Detail & Related papers  (2025-08-11T17:54:31Z)
• VLN-R1: Vision-Language Navigation via Reinforcement Fine-Tuning [77.34267241692706]
  Vision-Language Navigation (VLN) is a core challenge in embodied AI, requiring agents to navigate real-world environments using natural language instructions.<n>We propose VLN-R1, an end-to-end framework that leverages Large Vision-Language Models (LVLM) to directly translate egocentric video streams into continuous navigation actions.
  arXiv  Detail & Related papers  (2025-06-20T17:59:59Z)
• RoboCerebra: A Large-scale Benchmark for Long-horizon Robotic Manipulation Evaluation [59.9896841079005]
  We introduce RoboCerebra, a benchmark for evaluating high-level reasoning in long-horizon robotic manipulation.<n>The dataset is constructed via a top-down pipeline, where GPT generates task instructions and decomposes them into subtask sequences.<n>Compared to prior benchmarks, RoboCerebra features significantly longer action sequences and denser annotations.
  arXiv  Detail & Related papers  (2025-06-07T06:15:49Z)
• ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation [62.58034332427291]
  ForceVLA is a novel end-to-end manipulation framework.<n>It treats external force sensing as a first-class modality within VLA systems.
  arXiv  Detail & Related papers  (2025-05-28T09:24:25Z)
• ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model [21.844214660424175]
  ChatVLA is a novel framework featuring Phased Alignment Training, which incrementally integrates multimodal data after initial control mastery, and a Mixture-of-Experts architecture to minimize task interference.<n>ChatVLA demonstrates competitive performance on visual question-answering datasets and significantly surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks.<n>Our findings highlight the potential of our unified framework for achieving both robust multimodal understanding and effective robot control.
  arXiv  Detail & Related papers  (2025-02-20T10:16:18Z)
• Robotic Control via Embodied Chain-of-Thought Reasoning [86.6680905262442]
  Key limitation of learned robot control policies is their inability to generalize outside their training data.<n>Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models can substantially improve their robustness and generalization ability.<n>We introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features before predicting the robot action.
  arXiv  Detail & Related papers  (2024-07-11T17:31:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.