NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models

• URL: http://arxiv.org/abs/2603.09542v1
• Date: Tue, 10 Mar 2026 11:51:54 GMT
• Title: NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models
• Abstract summary: Vision-Language-Action (VLA) models are formulated to ground instructions in visual context and generate action sequences for robotic manipulation.<n>We propose a novel Neuro-Symbolic Vision-Language-Action (NS-VLA) framework via online reinforcement learning (RL)<n> Experiments on robotic manipulation benchmarks demonstrate that NS-VLA outperforms previous methods in both one-shot training and data-perturbed settings.
• Score: 12.699920959064656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models are formulated to ground instructions in visual context and generate action sequences for robotic manipulation. Despite recent progress, VLA models still face challenges in learning related and reusable primitives, reducing reliance on large-scale data and complex architectures, and enabling exploration beyond demonstrations. To address these challenges, we propose a novel Neuro-Symbolic Vision-Language-Action (NS-VLA) framework via online reinforcement learning (RL). It introduces a symbolic encoder to embedding vision and language features and extract structured primitives, utilizes a symbolic solver for data-efficient action sequencing, and leverages online RL to optimize generation via expansive exploration. Experiments on robotic manipulation benchmarks demonstrate that NS-VLA outperforms previous methods in both one-shot training and data-perturbed settings, while simultaneously exhibiting superior zero-shot generalizability, high data efficiency and expanded exploration space. Our code is available.

Related papers

• Decoupling the Declarative from the Procedural in Vision-Language-Action Models [65.22639791239932]
  Policy trained to clone a behavior from object-specific demonstrations must generalize beyond that object.<n>We propose w$2$VLA, a new Vision-Language-Action model with restructured information flow.<n>Unlike popular, state-of-the-art VLAs, we show that our modular approach successfully decouples knowledge representations.
  arXiv  Detail & Related papers  (2026-06-19T14:43:58Z)
• PokeVLA: Empowering Pocket-Sized Vision-Language-Action Model with Comprehensive World Knowledge Guidance [24.102770290097435]
  PokeVLA is a lightweight model for embodied manipulation that infuses vision-language understanding into action learning.<n>Our framework introduces a two-stage training paradigm: first, we pre-train a compact vision-language model (PokeVLM) on a multimodal dataset of 2.4M samples.<n>Experiments demonstrate state-of-the-art performance on the LIBERO-Plus benchmark and in real-world deployment.
  arXiv  Detail & Related papers  (2026-04-22T17:58:19Z)
• Build on Priors: Vision--Language--Guided Neuro-Symbolic Imitation Learning for Data-Efficient Real-World Robot Manipulation [4.118262876469644]
  We present a scalable neuro-symbolic framework that autonomously constructs symbolic planning domains and data-efficient control policies.<n>Our method segments demonstrations into skills and employs a Vision-Language Model (VLM) to classify skills.<n>Known controllers can be leveraged for real-world data augmentation by projecting a single demonstration onto other objects in the scene.
  arXiv  Detail & Related papers  (2026-04-04T15:17:59Z)
• ST4VLA: Spatially Guided Training for Vision-Language-Action Models [80.35847468618276]
  Large vision-language models (VLMs) excel at multimodal understanding but fall short when extended to embodied tasks.<n>We introduce ST4VLA, a dual-system Vision-Language-Action framework to align action learning with spatial priors inVLMs.<n> Empirically, ST4VLA achieves substantial improvements over vanilla VLA, with performance increasing from 66.1 -> 84.6 on Google Robot and from 54.7 -> 73.2 on WidowX Robot.
  arXiv  Detail & Related papers  (2026-02-10T18:59:17Z)
• LoLA: Long Horizon Latent Action Learning for General Robot Manipulation [38.209790870296835]
  Long Horizon Latent Action Learning (LoLA) is a framework designed for robot manipulation.<n>We first employ Vision-Language Models to encode rich contextual features from historical sequences and multi-view observations.<n>We then introduce State-Aware Latent Re-representation, which transforms visual inputs and language commands into actionable robot motion space.
  arXiv  Detail & Related papers  (2025-12-23T08:45:24Z)
• ReconVLA: Reconstructive Vision-Language-Action Model as Effective Robot Perceiver [35.25196177784228]
  We propose ReconVLA, a reconstructive VLA model with an implicit grounding paradigm.<n>Conditioned on the model's visual outputs, a diffusion transformer reconstructs the gaze region of the image.<n>This process prompts the VLA model to learn fine-grained representations and accurately allocate visual attention.
  arXiv  Detail & Related papers  (2025-08-14T04:20:19Z)
• 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)
• VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning [14.099306230721245]
  We present VLA-RL, an exploration-based framework that improves on online collected data at test time.<n>We fine-tune a pretrained vision-language model as a robotic process reward model, which is trained on pseudo reward labels annotated on automatically extracted task segments.<n>VLA-RL enables OpenVLA-7B to surpass the strongest finetuned baseline by 4.5% on 40 challenging robotic manipulation tasks in LIBERO.
  arXiv  Detail & Related papers  (2025-05-24T14:42:51Z)
• CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models [89.44024245194315]
  We introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs)<n>We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens.<n>Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks.
  arXiv  Detail & Related papers  (2025-03-27T22:23:04Z)
• OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles [91.88062410741833]
  We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning.<n>We show that OpenVLThinker-7B consistently advances performance across six benchmarks demanding mathematical and general reasoning.
  arXiv  Detail & Related papers  (2025-03-21T17:52:43Z)
• LLaRA: Supercharging Robot Learning Data for Vision-Language Policy [56.505551117094534]
  We introduce LLaRA: Large Language and Robotics Assistant, a framework that formulates robot action policy as visuo-textual conversations.<n>First, we present an automated pipeline to generate conversation-style instruction tuning data for robots from existing behavior cloning datasets.<n>We show that a VLM finetuned with a limited amount of such datasets can produce meaningful action decisions for robotic control.
  arXiv  Detail & Related papers  (2024-06-28T17:59:12Z)
• Vision-and-Language Navigation Generative Pretrained Transformer [0.0]
  Vision-and-Language Navigation Generative Pretrained Transformer (VLN-GPT) Adopts transformer decoder model (GPT2) to model trajectory sequence dependencies, bypassing the need for historical encoding modules. Performance assessments on the VLN dataset reveal that VLN-GPT surpasses complex state-of-the-art encoder-based models.
  arXiv  Detail & Related papers  (2024-05-27T09:42:04Z)
• MELD: Meta-Reinforcement Learning from Images via Latent State Models [109.1664295663325]
  We develop an algorithm for meta-RL from images that performs inference in a latent state model to quickly acquire new skills. MELD is the first meta-RL algorithm trained in a real-world robotic control setting from images.
  arXiv  Detail & Related papers  (2020-10-26T23:50:30Z)

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.