Structured Scene Memory for Vision-Language Navigation
Open accessAbstract
Recently, numerous algorithms have been developed to tackle the problem of vision-language navigation (VLN), i.e., entailing an agent to navigate 3D environments through following linguistic instructions. However, current VLN agents simply store their past experiences/observations as latent states in recurrent networks, failing to capture environment layouts and make long-term planning. To address these limitations, we propose a crucial architecture, called Structured Scene Memory (SSM). It is compartmentalized enough to accurately memorize the percepts during navigation. It also serves as a structured scene representation, which captures and disentangles visual and geometric cues in the environment. SSM has a collect-read controller that adaptively collects information for supporting current decision making and mimics iterative algorithms for long-range reasoning. As SSM provides a complete action space, i.e., all the navigable places on the map, a frontier-exploration based navigation decision making strategy is introduced to enable efficient and global planning. Experiment results on two VLN datasets (i.e., R2R and R4R) show that our method achieves state-of-the-art performance on several metrics.
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An agent trained with augmented instructions generated by a simple LSTM-based model can achieve good performance on multiple navigation tasks.
“an agent trained with augmented instructions generated by a simple LSTM-based model [74] can achieve good performance on multiple navigation tasks [10, 60, 75].”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [74]
Section 3.4 (page not available) says the agent uses back-translation-based data augmentation for R2R, but does not say that a simple LSTM-based model generates the augmented instructions. Its LSTM is described as producing instruction embeddings, not instructions. The separate citations after the performance claim are not attributed to this source here. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRLBench lacks real-world reproducibility because it is simulation-only, restricting its applicability in sim-to-real transfer.
“However, as RLBench is limited to simulation, it lacks real-world reproducibility, restricting its applicability in sim-to-real transfer.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [288]
The Results sections (§§4.1–4.2; page numbers unavailable) evaluate vision-language navigation on R2R and R4R, not RLBench. Searches for RLBench, simulation-only limitations, real-world reproducibility, and sim-to-real transfer found no passage addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredRLBench is built on PyRep (CoppeliaSim), provides diverse manipulation tasks with high-dimensional visual inputs, and is suited for end-to-end policy learning.
“RLBench [288] is built on PyRep (CoppeliaSim), a simulator that provides a diverse set of robotic manipulation tasks with high-dimensional visual inputs, making it well-suited for endto-end policy learning.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [288]
The paper’s §§4.1–4.2 discuss vision-language navigation on R2R and R4R, while §3.4 describes training a navigation agent. Searches for RLBench, PyRep, CoppeliaSim, manipulation tasks, and end-to-end policy learning found no passage addressing the attributed description. (LLM, conf=0.78)
Experimental — accuracy not yet measuredKitchenShift is useful for benchmarking imitation learning and reinforcement learning but does not support real-world reproducibility; its domain shift testing provides insight into real-world uncertainty modeling.
“KitchenShift [292] is particularly useful for benchmarking imitation learning (IL) and reinforcement learn-ing (RL) strategies, but like RLBench [288], it does not support real-world reproducibility, though its domain shift testing provides insight into real-world uncertainty modeling.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [288]
Sections 4.1 and 4.2 evaluate vision-language navigation on R2R and R4R, not KitchenShift. Searches for KitchenShift, domain shift, and real-world reproducibility found no passage addressing the claimed benchmark or its properties; the paper’s mention of a kitchen is a navigation example. (LLM, conf=0.78)
Experimental — accuracy not yet measuredRLBench is restricted to simulated tasks because of CoppeliaSim physics limitations, creating challenges for sim-to-real transfer.
“However, due to the limitations of Cop-peliaSim's physics engine, RLBench [288] remains restricted to purely simulated tasks, posing challenges for sim-to-real transfer.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [288]
Section 4.1 describes experiments on the R2R vision-language navigation dataset, and Section 4.2 describes R4R; neither addresses RLBench. Searches for RLBench, CoppeliaSim physics limitations, and sim-to-real transfer found no passage in our copy that speaks to the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
RLBench is one of the most widely used manipulation benchmarks, offering over 100 tasks from basic grasping to tool use and assembly, with task diversity, scalability, and demonstration datasets suitable for reinforcement learning, imitation learning, and traditional control.
“RLBench [288] is one of the most widely used robotic manipulation benchmarks, offering 100+ tasks ranging from basic object grasping to complex tool use and assembly tasks.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [288]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA simple LSTM-based model (EnvDrop) trained from scratch results in a higher Bleu-4 score than a fine-tuned GPT-2.
“a simple LSTMbased model (EnvDrop [74]) trained from-scratch results in a higher Bleu-4 score [57] than a fine-tuned GPT-2 [65].”
From Scaling Data Generation in Vision-and-Language Navigation (2023) · cites this paper as [74]
Section 4.1 compares E-Dropout with other vision-language navigation models, but reports navigation metrics, not BLEU-4 scores. Searches for EnvDrop, GPT-2, BLEU-4, and fine-tuning found no passage comparing their scores. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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References 61 · Cited by 123
Research works printed but not linked: 10
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- Authors
- Wang, H., Wang, W., Liang, W., Xiong, C., & Shen, J.
- Affiliations
- Beijing Institute of Technology (CN)
- ETH Zurich (CH)
- Salesforce (United States) (US)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 8451-8460
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3133869372
- Cited by
- 123
- References
- 61
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 26 · 2023: 33 · 2022: 22 · 2021: 9
- FWCI
- 7.36
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Beijing Institute of Technology
- Ministry of Education
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Keywords
- vision-and-language navigation, scene representation, long-term planning, frontier exploration, robot navigation
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