SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models

• URL: http://arxiv.org/abs/2608.08839v1
• Date: Sun, 09 Aug 2026 17:59:35 GMT
• Title: SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models
• Abstract summary: We propose SG-WAM, a semantic guidance method for world-action models.<n>We use a vision-language model (VLM) as a semantic planner to enhance the instruction-grounding capacity of world-action models.
• Score: 16.792979070880147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: World-Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation. However, most existing WAMs generate future videos and actions by relying mainly on visual cues rather than language instructions, since off-the-shelf text encoders embed instructions independently of visual observations. As a result, the videos predicted by these WAMs are often semantically misaligned with their corresponding language instructions, which degrades the accuracy of the predicted actions. To overcome this limitation, we propose SG-WAM, a semantic guidance method for world-action models that leverages a vision-language model (VLM) as a semantic planner to enhance the instruction-grounding capacity of world-action models. Specifically, we train a VLM-based planner to predict text-grounded and spatial-aware semantic foresight. The text-grounded semantic foresight grounds the instruction by identifying the correct target objects, and the spatial-aware semantic foresight provides the scene geometry for precise manipulation. We then inject this foresight into the world-action model as high-level semantic guidance, ensuring that both future-video generation and action prediction faithfully follow the language instruction. Extensive experiments in simulation and the real world demonstrate the superiority of our semantic guidance method, showcasing precise manipulation and strong instruction-following capabilities.

Related papers

• AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding [55.535374902204296]
  AffordanceVLA is a unified framework that introduces structured affordance forecasting as a task-oriented intermediate representation.<n>We show that AffordanceVLA achieves strong performance across diverse manipulation scenarios.
  arXiv  Detail & Related papers  (2026-06-04T13:28:51Z)
• AwareVLN: Reasoning with Self-awareness for Vision-Language Navigation [78.80536515102305]
  Vision-and-Language Navigation (VLN) requires an agent to ground language instructions to its own movement within a visual environment.<n>We propose AwareVLN, a novel framework that equips the navigation model with a self-aware reasoning mechanism.<n>Our approach features two key innovations: (1) a structural reasoning module that fosters spatial and task-oriented self-awareness, and (2) an automatic data engine with progress division for effective training.
  arXiv  Detail & Related papers  (2026-05-21T17:58:26Z)
• From Human Videos to Robot Manipulation: A Survey on Scalable Vision-Language-Action Learning with Human-Centric Data [71.22409934108924]
  Human videos are abundant and capture rich interactions, providing diverse semantic and physical cues for real-world manipulation.<n>This survey provides a unified view of how human videos are transformed into effective knowledge for Vision-Language-Action (VLA) models.<n>We highlight three key open challenges in this area: structuring unstructured videos into training-ready episodes, grounding video-derived supervision into robot-executable actions under embodiment and viewpoint heterogeneity, and designing evaluation protocols that better predict real-world deployment performance and transfer efficiency.
  arXiv  Detail & Related papers  (2026-05-18T06:19:16Z)
• Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulation [31.386822229629455]
  We propose Language-Guided Grasp Detection (LGGD) with a coarse-to-fine learning paradigm for robotic manipulation.<n>This design enables fine-grained visual-semantic alignment and improves the feasibility of the predicted grasps with respect to task instructions.<n>Experiments on the OCID-VLG and Grasp-Anything++ datasets show that LGGD surpasses existing language-guided grasping methods.
  arXiv  Detail & Related papers  (2025-12-24T09:16:42Z)
• Executable Analytic Concepts as the Missing Link Between VLM Insight and Precise Manipulation [70.8381970762877]
  Vision-Language Models (VLMs) have demonstrated remarkable capabilities in semantic reasoning and task planning.<n>We introduce GRACE, a novel framework that grounds VLM-based reasoning through executable analytic concepts.<n>G GRACE provides a unified and interpretable interface between high-level instruction understanding and low-level robot control.
  arXiv  Detail & Related papers  (2025-10-09T09:08:33Z)
• DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge [41.030494146004806]
  We propose DreamVLA, a novel VLA framework that integrates comprehensive world knowledge forecasting to enable inverse dynamics modeling.<n>DreamVLA introduces a dynamic-region-guided world knowledge prediction, integrated with the spatial and semantic cues, which provide compact yet comprehensive representations for action planning.<n>Experiments on both real-world and simulation environments demonstrate that DreamVLA achieves 76.7% success rate on real robot tasks.
  arXiv  Detail & Related papers  (2025-07-06T16:14:29Z)
• OTTER: A Vision-Language-Action Model with Text-Aware Visual Feature Extraction [95.6266030753644]
  Vision-Language-Action (VLA) models aim to predict robotic actions based on visual observations and language instructions.<n>Existing approaches require fine-tuning pre-trained vision-language models (VLMs) as visual and language features are independently fed into downstream policies.<n>We propose OTTER, a novel VLA architecture that leverages existing alignments through explicit, text-aware visual feature extraction.
  arXiv  Detail & Related papers  (2025-03-05T18:44:48Z)
• FILS: Self-Supervised Video Feature Prediction In Semantic Language Space [11.641926922266347]
  This paper demonstrates a self-supervised approach for learning semantic video representations. We present FILS, a novel self-supervised video Feature prediction In semantic Language Space.
  arXiv  Detail & Related papers  (2024-06-05T16:44:06Z)
• Object-Centric Instruction Augmentation for Robotic Manipulation [29.491990994901666]
  We introduce the textitObject-Centric Instruction Augmentation (OCI) framework to augment highly semantic and information-dense language instruction with position cues. We utilize a Multi-modal Large Language Model (MLLM) to weave knowledge of object locations into natural language instruction. We demonstrate that robotic manipulator imitation policies trained with our enhanced instructions outperform those relying solely on traditional language instructions.
  arXiv  Detail & Related papers  (2024-01-05T13:54:45Z)
• A Picture is Worth a Thousand Words: Language Models Plan from Pixels [53.85753597586226]
  Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments. In this work, we explore the use of pre-trained language models (PLMs) to reason about plan sequences from text instructions in embodied visual environments.
  arXiv  Detail & Related papers  (2023-03-16T02:02:18Z)

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.