Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation

• URL: http://arxiv.org/abs/2606.29908v1
• Date: Mon, 29 Jun 2026 07:43:47 GMT
• Title: Pondering the Way: Spatial-perceiving World Action Model for Embodied Navigation
• Abstract summary: We propose SWAM (Spatial-perceiving World Action Model), a task-centric joint observation-action generation framework.<n>Given start and goal RGB observations, SWAM performs single-pass inference to simultaneously generate intermediate RGB-D sequences and corresponding action trajectories.<n>We show that SWAM significantly outperforms state-of-the-art two-stage planners in success rate, trajectory accuracy, and inference efficiency.
• Score: 24.310993695214094
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing world model-based planners for visual navigation typically follow a verification-centric paradigm, decoupling goal intent from trajectory synthesis. This approach suffers from candidate dependence, heavy computational overhead, and inconsistencies between sampled actions and predicted visuals. To address these issues, we propose SWAM (Spatial-perceiving World Action Model), a task-centric joint observation-action generation framework. Given start and goal RGB observations, SWAM performs single-pass inference to simultaneously generate intermediate RGB-D sequences and corresponding action trajectories, promoting goal-consistent trajectory generation and improved spatial feasibility. While SWAM leverages depth pseudo-labels during training to internalize spatial priors, it requires only monocular RGB input at inference time. We further introduce a visual-guided action refinement module and a trajectory-scale regularization loss to enforce fine-grained alignment between motion and visual cues while stabilizing predictions across varying distances. Extensive experiments show that SWAM significantly outperforms state-of-the-art two-stage planners in success rate, trajectory accuracy, and inference efficiency, while demonstrating robust zero-shot generalization to unseen environments.

Related papers

• DyG$^2$T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer [86.4618122245946]
  DyG$2$T is a dynamics modeling framework that infers object motion trajectories by spatially completing and temporally discriminating Key Point representations.<n>Experiments on both synthetic and real-world datasets demonstrate that DyG$2$T achieves accurate dynamics modeling and reasoning.
  arXiv  Detail & Related papers  (2026-08-19T03:44:28Z)
• WAM-Nav: Asymmetric Latent World-Action Modeling for Unified Visual Navigation [31.539730066120374]
  Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints.<n>We propose WAM-Nav, a Latent World-Action Model for embodied visual navigation that jointly learns action generation and latent visual foresight.
  arXiv  Detail & Related papers  (2026-06-03T14:05:19Z)
• HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models [58.191567345416836]
  World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics.<n>HarmoWAM fully leverages a world model to unify predictive and reactive control, enabling both general transit and precise manipulation.<n>We construct three training-unseen test environments across six real-world robotic tasks, covering variations in background, position, and object semantics.
  arXiv  Detail & Related papers  (2026-05-11T17:59:56Z)
• CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics [25.518388341069492]
  We propose CLaD, a framework that models how proprioceptive and semantic states jointly evolve under actions.<n>CLaD predicts grounded latent foresights via self-supervised objectives with EMA target encoders and auxiliary reconstruction losses.<n>On LIBERO-LONG benchmark, CLaD achieves 94.7% success rate, competitive with large VLAs with significantly fewer parameters.
  arXiv  Detail & Related papers  (2026-03-31T08:13:45Z)
• \textsc{NaVIDA}: Vision-Language Navigation with Inverse Dynamics Augmentation [50.027425808733994]
  textscNaVIDA is a unified VLN framework that couples policy learning with action-grounded visual dynamics and adaptive execution.<n>textscNaVIDA augments training with chunk-based inverse-dynamics supervision to learn causal relationship between visual changes and corresponding actions.<n>Experiments show that textscNaVIDA achieves superior navigation performance compared to state-of-the-art methods with fewer parameters.
  arXiv  Detail & Related papers  (2026-01-26T06:16:17Z)
• AstraNav-World: World Model for Foresight Control and Consistency [40.07910402326578]
  Embodied navigation in dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time.<n>We propose AstraNav-World, an end-to-end world model that jointly reasons about future visual states and action sequences.<n>Our framework integrates a diffusion-based video generator with a vision-language policy, enabling synchronized rollouts.
  arXiv  Detail & Related papers  (2025-12-25T15:31:24Z)
• Aerial Vision-Language Navigation with a Unified Framework for Spatial, Temporal and Embodied Reasoning [5.517595398768408]
  We present a unified aerial VLN framework that operates solely on ego monocular RGB observations and natural language instructions.<n>This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery.
  arXiv  Detail & Related papers  (2025-12-09T14:25:24Z)
• GoViG: Goal-Conditioned Visual Navigation Instruction Generation [69.79110149746506]
  We introduce Goal-Conditioned Visual Navigation Instruction Generation (GoViG), a new task that aims to autonomously generate precise and contextually coherent navigation instructions.<n>GoViG exclusively leverages raw egocentric visual data, substantially improving its adaptability to unseen and unstructured environments.
  arXiv  Detail & Related papers  (2025-08-13T07:05:17Z)
• RSRNav: Reasoning Spatial Relationship for Image-Goal Navigation [57.197881161006904]
  Recent image-goal navigation (ImageNav) methods learn a perception-action policy by separately capturing semantic features of the goal and egocentric images.<n>We propose RSRNav, a simple yet effective method that reasons spatial relationships between the goal and current observations as navigation guidance.
  arXiv  Detail & Related papers  (2025-04-25T00:22:17Z)
• EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception [57.54227977352417]
  Event cameras emit high-speed event stream for perception tasks.<n>We propose a novel approach named EHGCN to perceive event stream in both Euclidean and hyperbolic spaces.
  arXiv  Detail & Related papers  (2025-04-23T11:01:03Z)
• Ground-level Viewpoint Vision-and-Language Navigation in Continuous Environments [10.953629652228024]
  Vision-and-Language Navigation (VLN) agents associate time-sequenced visual observations with corresponding instructions to make decisions.<n>In this paper, we address the mismatch between human-centric instructions and quadruped robots with a low-height field of view.<n>We propose a Ground-level Viewpoint Navigation (GVNav) approach to mitigate this issue.
  arXiv  Detail & Related papers  (2025-02-26T10:30:40Z)
• MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction [22.553356096143734]
  We propose a novel generative flow based framework with dual graphormer for pedestrian trajectory prediction (STGlow) Our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-21T07:29:24Z)

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.