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VISTA: Generative Visual Imagination for Vision-and-Language Navigation

Yanjia Huang, Mingyang Wu, Renjie Li, Zhengzhong Tu

arXiv:2505.07868v2cs.RO

TL;DR

VLN agents often rely on immediate observations, limiting long-horizon navigation when future states are unseen. VISTA uses adaptive visual imagination, perceptual alignment, and structured reasoning to anticipate goals and guide actions. It reports state-of-the-art results on R2R and RoboTHOR, including a +3.2% SR gain over DUET-Imagine on R2R Validation Unseen.

  • Problem

    Existing VLN agents typically react to immediate observations and static instructions without explicitly modeling unseen future states, limiting long-horizon navigation.

    Source: §1 · p. 1

  • Method

    VISTA combines adaptive instruction- or observation-driven imagination, perceptual alignment of imagined goals with current observations, and structured Chain-of-Thought action reasoning.

    Source: §1 · p. 2, §3 · p. 3, §3.1 · p. 4

  • Results

    +3.2% SR over DUET-Imagine is reported on R2R Validation Unseen, while VISTA achieves state-of-the-art results across R2R and RoboTHOR.

    Source: §4.2 · p. 7, §5 · p. 8

  • Takeaways & Limitations

    Integrating forward-looking imagination, perceptual grounding, and structured reasoning supports robust and interpretable navigation decisions in long-horizon environments.

    Source: §1 · p. 2, §5 · p. 8

    VISTA depends on diffusion-model fidelity, adds computational overhead, uses manually tuned scheduler thresholds, and is evaluated only in simulation.

    Source: §6 · p. 8

Abstract

from arXiv · show

Vision-and-Language Navigation (VLN) tasks agents with locating specific objects in unseen environments using natural language instructions and visual cues. Many existing VLN approaches typically follow an 'observe-and-reason' schema, that is, agents observe the environment and decide on the next action to take based on the visual observations of their surroundings. They often face challenges in long-horizon scenarios due to limitations in immediate observation and vision-language modality gaps. To overcome this, we present VISTA, a novel framework that employs an 'imagine-and-align' navigation strategy. Specifically, we leverage the generative prior of pre-trained diffusion models for dynamic visual imagination conditioned on both local observations and high-level language instructions. A Perceptual Alignment Filter module then grounds these goal imaginations against current observations, guiding an interpretable and structured reasoning process for action selection. Experiments show that VISTA sets new state-of-the-art results on Room-to-Room (R2R) and RoboTHOR benchmarks, e.g.,+3.6% increase in Success Rate on R2R. Extensive ablation analysis underscores the value of integrating forward-looking imagination, perceptual alignment, and structured reasoning for robust navigation in long-horizon environments.

1 Introduction

VLN agents often react to immediate observations without explicitly modeling unseen future states, creating challenges for long-horizon navigation. VISTA addresses this gap through imagination, perceptual alignment, and structured reasoning.

  • Existing VLN agents typically make local predictions from immediate observations and static instructions rather than modeling what lies beyond view.Source: §1 · p. 1
  • Explicit goal imagination is proposed to help agents anticipate future states and make more informed navigation decisions under uncertainty.Source: §1 · p. 1
  • VISTA combines an Adaptive Imagination Scheduler, Perceptual Alignment Filter, and language-grounded Chain-of-Thought reasoning in a closed-loop framework.Source: §1 · p. 2
  • AIS selects instruction-driven or observation-driven imagination modes, while PAF grounds imagined goals against current observations with interpretable attention maps.Source: §1 · p. 2
  • VISTA is evaluated on R2R and RoboTHOR, with reported gains in success rate, navigation efficiency, and interpretability.Source: §1 · p. 2

2 Related Work

Related work spans VLN systems, visual imagination, and structured reasoning. VISTA differs by embedding visual imagination and perceptual alignment directly into navigation reasoning rather than treating generated content as auxiliary information.

2.1 Vision-and-Language Navigation (VLN)

  • VLN benchmarks and simulators support navigation research, while recent pretraining and LLM-based methods target generalization and long-range planning.Source: §2.1 · p. 2
  • Existing LLM-based VLN agents still struggle with realistic commonsense-rich environments and often lack interpretable, structured decision-making.Source: §2.1 · p. 2

2.2 Visual Imagination in VLN

  • Prior visual-imagination methods use GANs or diffusion models to predict future views, but commonly rely on offline generation and auxiliary use of imagined content.Source: §2.2 · pp. 2–3

2.3 Chain of Thoughts Reasoning

  • CoT, Tree-of-Thoughts, self-consistency, and navigation-specific reasoning methods decompose navigation decisions into more interpretable reasoning paths.Source: §2.3 · p. 3
  • VISTA distinguishes itself by embedding visual imagination and perceptual alignment within CoT to create visually grounded reasoning steps.Source: §2.3 · p. 3, Figure 1 · p. 3

3 Method

VISTA forms a closed loop that predicts future goals, generates visual hypotheses, aligns them with current observations, and reasons over actions. Its modules address uncertainty, visual grounding, and interpretable control.

3 Method

  • VISTA selects an imagination mode, generates a visual goal hypothesis, aligns it with current input, and uses the aligned result to choose the next action.Source: §3 · pp. 3–4

3.1 Adaptive Imagination Scheduler

  • AIS switches between instruction-driven and observation-driven goal prediction because instructions provide semantic guidance while observations provide detailed but potentially noisy local evidence.Source: §3.1 · p. 4
  • AIS bases mode selection on trajectory uncertainty and visual-semantic similarity, using action entropy, path deviation, and cosine similarity between imagined and observed images.Source: §3.1 · p. 4

3.2 Visual Imagination Module

  • The visual imagination module captions candidate scenes with Qwen2-VL, synthesizes corresponding scenes with LoRA-finetuned Stable Diffusion, and creates inpainted observations.Source: §3.2 · pp. 4–5

3.3 Perceptual Alignment Filter

  • PAF compares current, imagined, and inpainted images through shared visual encoding and attention fusion to produce a spatial map of likely goal regions.Source: §3.3 · p. 5
  • PAF supervision uses entity keywords localized by Grounded-SAM, turning imagined objects into masks that indicate where they should appear in the real observation.Source: §3.3 · p. 5

3.4 Navigational Chain-of-Thought Decision

  • The navigation CoT process grounds the goal, verifies it perceptually, and justifies the selected action using instructions, captions, attention maps, and history.Source: §3.4 · p. 5
  • +3.2% SR over DUET-Imagine is reported on R2R, with VISTA achieving state-of-the-art performance and lower navigation error.Source: Table 1 · p. 6

4 Experiments

Experiments evaluate VISTA on R2R and RoboTHOR, where it achieves strong unseen-environment performance and remains robust on long paths. Ablations show that imagination, perceptual alignment, and structured reasoning contribute distinct capabilities.

4.1 Experimental Setup

  • R2R evaluation uses 90 Matterport3D scenes, 10,567 panoramic viewpoints, and 7,189 instruction-annotated trajectories, with an auxiliary R2R-Imagine dataset exceeding 250,000 images.VISTA runs at an average speed of 8 FPS on an NVIDIA A100 GPU during inference.Source: §4.1 · pp. 6–7
  • Figure 4 combines module-removal bars with a trajectory-level scatter plot to examine ablations and robustness across path lengths.The supplied caption identifies the visual’s two analyses but does not provide additional plotted values.Source: Figure 4 · p. 7

4.2 Main Results

  • 77.8% SR and 68.3 SPL on R2R Validation Unseen surpass DUET-Imagine by +3.2% SR and +7.5 SPL, while Test Unseen reaches 74.9% SR.On RoboTHOR, VISTA reaches 28.8% SWPL and 43.1% SR.Source: §4.2 · p. 7
  • VISTA maintains higher success rates, more efficient paths, and more stable confidence as path lengths increase, supporting robustness on long-horizon tasks.The analysis attributes this behavior to combining global imagination, local grounding, and structured reasoning.Source: §4.2 · p. 7, Figure 4 · p. 7

4.3 Ablation Studies

  • Removing visual imagination causes the most severe performance drop, lowering success and increasing localization error because the agent cannot reason beyond its current field of view.On RoboTHOR paths longer than 15m, visual imagination improves SR by +11.1%.Source: §4.3 · p. 8
  • Removing CoT mildly affects success rate but reduces path efficiency, whereas removing the perceptual alignment filter weakens spatial focus and produces more erratic trajectories.These ablations assign distinct roles to structured reasoning and spatial grounding within the closed-loop system.Source: §4.3 · p. 8

5 Conclusion

VISTA concludes by presenting closed-loop imagination, perceptual grounding, and structured reasoning as a unified navigation process. The framework achieves state-of-the-art results on R2R and RoboTHOR, while ablations show distinct contributions from its modules.

5 Conclusion

  • VISTA integrates visual imagination, perceptual grounding, and structured reasoning into a closed-loop decision process for navigation.An adaptive scheduler selects static or dynamic goal prediction using navigation uncertainty and visual-semantic alignment.Source: §5 · p. 8
  • VISTA achieves state-of-the-art performance on R2R and RoboTHOR, particularly in long-horizon and visually ambiguous scenarios.The conclusion attributes distinct contributions to imagination, filtering, and reasoning through ablation and qualitative analysis.Source: §5 · p. 8, Table 2 · p. 8
  • The approach is presented as a general framework for visually grounded, imagination-driven planning beyond VLN.The paper connects generation and reasoning to embodied agents operating under uncertainty.Source: §5 · p. 8

6 Limitations

VISTA’s limitations concern generation fidelity, computational cost, scheduler tuning, and the gap between simulation evaluation and real-world deployment.

6 Limitations

  • VISTA’s imagined goal scenes may misalign in cluttered or diverse environments, while generation and reasoning add computational overhead that can limit real-time deployment.The scheduler also relies on manually tuned thresholds that may require adaptation across environments.Source: §6 · p. 8
  • Because all evaluations are conducted in simulation, real-world deployment may face sensor noise and domain shifts.The paper suggests lightweight generation, learned scheduling, and transfer methods as future directions.Source: §6 · p. 8

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