Pith. sign in

REVIEW 3 major objections 2 minor 2 cited by

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation

T0 review · 3 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper claims that an end-to-end conditional diffusion policy, trained with DAgger and with no waypoint predictor, substantially outperforms prior state-of-the-art two-stage waypoint-based models on VLN-CE benchmarks.

desk verdict Plausible waypoint-free diffusion policy for VLN-CE; the DAgger expert source is the load-bearing unknown, and the abstract ships no numbers. read the letter →

arxiv 2508.09444 v1 pith:HLJDHEBS submitted 2025-08-13 cs.RO cs.CV

Haoxiang Shi , Xiang Deng , Zaijing Li , Gongwei Chen , Yaowei Wang , Liqiang Nie This is my paper · ORCID
classification cs.ROcs.CV
keywords Vision-LanguageNavigationVLN-CEDiffusionPolicyDAggerImitationLearningWaypointPredictionContinuousEnvironmentsEmbodied
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Vision-language navigation in continuous environments (VLN-CE) has usually been split into two stages: a waypoint predictor proposes candidate waypoints, then a planner picks among them. The paper tries to show that this split is unnecessary and even harmful. It proposes DifNav, a single conditional diffusion policy that maps the instruction and observations directly to a distribution over future actions in continuous space, removing the waypoint predictor entirely. The policy is trained with DAgger, which adds expert relabelled trajectories from the agent's own visited states and then fine-tunes on the aggregated data. If the paper's results hold, a unified end-to-end diffusion policy can outperform the two-stage architecture, simplifying navigation systems and capturing multiple valid instruction-following behaviors at once.

What carries the argument

The central object is a conditional diffusion policy: a diffusion model conditioned on the language instruction, visual observations, and navigation history, whose denoising output is a distribution over continuous actions. It replaces the separate waypoint predictor and planner with a single policy that can represent several valid next actions for one instruction. The supporting mechanism is DAgger (Dataset Aggregation): during online rollouts an expert relabels the agent's own visited states with corrective actions, and the policy is fine-tuned on these aggregated expert trajectories to recover from error states. The diffusion model supplies multi-modality; DAgger supplies error recovery.

What would settle it

Inspect the DAgger expert in the released code and check whether it is a two-stage waypoint-based planner, a ground-truth navigation-graph oracle, or otherwise uses privileged waypoint information. If it does, the claim that the method needs no waypoint predictor is not established. A second check is to retrain DifNav with expert trajectories from a simple geometric planner instead: if navigation metrics drop substantially, the expert source is carrying the gain.

Watch

Extended reading notes

Core claim

DifNav is an end-to-end conditional diffusion policy for VLN-CE. Instead of first generating waypoints and then planning over them, it models multi-modal action distributions over future actions directly in continuous navigation space, so waypoint generation and planning happen inside one denoising process. DAgger is used during online policy training: an expert relabels the states the agent actually visits, producing additional training trajectories, and the aggregated dataset is used to fine-tune the policy. This addresses compounding error from imitation learning and improves long-horizon spatial reasoning. Across benchmark datasets, DifNav is reported to substantially outperform prior st

Load-bearing premise

The load-bearing premise is that the expert used to generate DAgger trajectories does not itself rely on a waypoint predictor or privileged waypoint information; if it does, the reported gains could come from the expert data rather than from the end-to-end diffusion policy.

Editorial extensions

If this is right

  • If correct, a VLN-CE agent can navigate without any separate waypoint predictor, simplifying the architecture and removing the bottleneck where poor waypoint candidates limit later planning.
  • DAgger-based online relabelling is load-bearing: it is the mechanism that lets the policy recover from its own mistakes and handle long-horizon instructions, so removing it should degrade performance in long episodes.
  • One diffusion policy can represent several plausible instruction-following behaviors in the same situation, matching the multi-modal nature of natural-language navigation better than a deterministic two-stage pipeline.
  • The unified policy establishes a new comparison point: future VLN-CE methods should be measured against an end-to-end diffusion baseline, not only against two-stage waypoint models.
  • The policy does not need waypoint labels, so it is applicable where discrete waypoint supervision is unavailable or expensive to annotate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves the identity of the DAgger expert unspecified; a decisive extension would rerun the method with experts of varying privilege (for example, a geometric path planner versus a waypoint-based teacher) to attribute the gain to the diffusion policy rather than to the expert data.
  • If the result transfers, the two-stage waypoint decomposition may be an artifact of offline imitation learning rather than a requirement of the task, suggesting that other long-horizon embodied tasks with discrete subgoal hierarchies could also be unified into single diffusion policies.
  • DAgger may matter more for stochastic diffusion policies than for deterministic policies, because corrective relabelling at states the sampler actually visits stabilises the learned action distribution; this is a testable hypothesis the paper does not directly investigate.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes DifNav, an end-to-end conditional diffusion policy for Vision-Language Navigation in Continuous Environments (VLN-CE). The stated contribution is to replace the conventional two-stage waypoint-predictor-plus-planner architecture with a single diffusion policy that directly models continuous action distributions, and to train it with DAgger for online policy improvement and expert trajectory augmentation. The abstract claims that, even without a waypoint predictor, DifNav 'substantially outperforms' previous state-of-the-art two-stage waypoint-based models on benchmark datasets. The review copy provided to me contains only the abstract; the full text body is empty, so the method and experimental details are not available for evaluation.

Significance. If the empirical claim is substantiated, the conceptual contribution is meaningful: unifying waypoint generation and planning into a single diffusion policy could mitigate the error accumulation and proxy-objective problems of two-stage VLN-CE systems, and the use of DAgger to reduce compounding imitation error is a sensible training strategy. The availability of code is a positive step for reproducibility. However, the significance cannot be assessed from the material provided, because the abstract contains no quantitative results, no baseline names, no dataset splits, and no statistical comparisons. The central 'no waypoint predictor' claim also depends on a detail—the source of DAgger expert trajectories—that is absent from the abstract.

major comments (3)
  1. [Abstract] The central claim—'substantially outperforms previous state-of-the-art two-stage waypoint-based models'—is made without any quantitative support. The abstract does not report success rate, SPL, navigation error, or any comparison numbers, nor does it name the baselines or datasets. For an empirical paper this is load-bearing: the verdict depends entirely on the experiments, which are not available in the review copy. Please provide the full experimental details and results.
  2. [Abstract] The DAgger expert is not specified. Since the method claims to 'eliminate the need for a waypoint predictor,' it is critical to know whether the expert trajectories are generated by a waypoint-based two-stage policy or use privileged/oracle waypoints. If so, the waypoint dependence is shifted into the training distribution, and the comparison against two-stage baselines could be confounded by the expert data rather than by the end-to-end architecture. This is exactly the concern raised by the stress-test note, and it lands: the abstract leaves the expert source unspecified.
  3. [Full text (missing)] The review copy contains only the abstract; the body—method description, training objective, DAgger aggregation schedule, diffusion architecture, hyperparameters, environment details, result tables, and statistical analyses—is absent. Without these materials I cannot verify the 'end-to-end' claim, the fairness of the comparisons, or the robustness of the reported improvements. This is not a minor presentation issue; it blocks evaluation entirely.
minor comments (2)
  1. [Abstract] The phrase 'substantially outperforms' should be replaced by concrete metric values with confidence intervals or standard deviations once the numerical results are provided.
  2. [Abstract] The two 'stages' of the existing framework are described only vaguely ('waypoint predictor' and 'navigation planner'). Naming concrete components and how they interact would help a reader evaluate the claimed unification.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identified: the paper's claim is an empirical benchmark result and the abstract provides no derivation chain that reduces the result to its inputs.

full rationale

The central claim is that an end-to-end conditional diffusion policy trained with DAgger, without a waypoint predictor, substantially outperforms two-stage waypoint-based models on VLN-CE benchmarks. This is an empirical claim supported by experiments, not a derivation from first principles. The only potentially circular element would be if the DAgger expert trajectories were themselves produced by the two-stage waypoint-based pipeline that the method claims to remove, or if the reported improvements were constructed from fitted quantities. However, the abstract does not specify the expert source, does not describe any equation-level reduction, and does not invoke self-citations or uniqueness theorems. The absence of a specified expert is an attribution and evaluation concern—it affects interpretability of the comparison—but it is not a demonstrable circular step under the required standard of quoting paper text and exhibiting a specific reduction. No load-bearing step reduces, by construction, to its own input. Therefore the appropriate score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

Abstract-only audit: no equations or model details are available. The listed axioms are the minimal background assumptions the abstract implies; free parameters are training hyperparameters whose values are not disclosed in the abstract.

free parameters (2)
  • DAgger aggregation ratio
    Amount of expert trajectory data mixed into the policy buffer during DAgger updates; hand-chosen and affects the final policy's behavior; not stated in the abstract.
  • Diffusion denoising step count
    Number of reverse diffusion steps used at inference; affects action stochasticity and quality; not reported in abstract.
assumptions (3)
  • domain assumption The VLN-CE simulator's continuous action space is compatible with the diffusion policy's action distribution and the reported navigation metrics are a valid measure of instruction-following.
    The paper trains and evaluates in VLN-CE; if the simulator's action execution or metrics do not reflect true navigation competence, the central claim may not generalize.
  • domain assumption An expert or oracle is available during training to generate DAgger trajectories.
    DAgger's success depends on access to expert demonstrations; the abstract does not specify whether this expert is the two-stage waypoint model the paper claims to replace, which is a key unstated assumption.
  • standard math Standard diffusion training via denoising score matching learns the conditional action distribution.
    The method relies on established diffusion model theory; correctness is assumed from prior literature rather than derived here.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation." pith.science (2026). https://pith.science/paper/HLJDHEBS

@misc{pith2026250809444,
  author       = {Pith},
  title        = {Pith review of: DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HLJDHEBS}},
  note         = {Machine review of arXiv:2508.09444}
}
read the original abstract

Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typically use a two-stage waypoint planning framework, where a high-level waypoint predictor generates the navigable waypoints, and then a navigation planner suggests the intermediate goals in the high-level action space. However, this two-stage decomposition framework suffers from: (1) global sub-optimization due to the proxy objective in each stage, and (2) a performance bottleneck caused by the strong reliance on the quality of the first-stage predicted waypoints. To address these limitations, we propose DAgger Diffusion Navigation (DifNav), an end-to-end optimized VLN-CE policy that unifies the traditional two stages, i.e. waypoint generation and planning, into a single diffusion policy. Notably, DifNav employs a conditional diffusion policy to directly model multi-modal action distributions over future actions in continuous navigation space, eliminating the need for a waypoint predictor while enabling the agent to capture multiple possible instruction-following behaviors. To address the issues of compounding error in imitation learning and enhance spatial reasoning in long-horizon navigation tasks, we employ DAgger for online policy training and expert trajectory augmentation, and use the aggregated data to further fine-tune the policy. This approach significantly improves the policy's robustness and its ability to recover from error states. Extensive experiments on benchmark datasets demonstrate that, even without a waypoint predictor, the proposed method substantially outperforms previous state-of-the-art two-stage waypoint-based models in terms of navigation performance. Our code is available at: https://github.com/Tokishx/DifNav.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation

    cs.RO 2026-01 conditional novelty 6.0 of 10

    SIDP trains a diffusion policy for visual navigation by reward-weighting its own sampled trajectories, improving success rate and cutting inference latency.

  2. A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation

    cs.RO 2026-07 accept novelty 5.5 of 10

    VLN methods show a large sim-to-real gap; a hierarchical system reaches 51% real-world success versus 22% for a monolithic RGB-only system across ten physical scenes.

Reference graph

Works this paper leans on

50 extracted references · 39 canonical work pages · cited by 2 Pith papers

  1. [1]

    Anderson, Q

    P. Anderson, Q. Wu, D. Teney, J. Bruce, M. Johnson, N. S \"u nderhauf, I. Reid, S. Gould, and A. Van Den Hengel, ``Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments,'' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3674--3683

    work page 2018

  2. [2]

    X. Wang, Q. Huang, A. Celikyilmaz, J. Gao, D. Shen, Y.-F. Wang, W. Y. Wang, and L. Zhang, ``Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation,'' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 6629--6638

    work page 2019

  3. [3]

    Y. Hong, Q. Wu, Y. Qi, C. Rodriguez-Opazo, and S. Gould, ``Vln bert: A recurrent vision-and-language bert for navigation,'' in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition, 2021, pp. 1643--1653

    work page 2021

  4. [4]

    Chen, P.-L

    S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev, ``Think global, act local: Dual-scale graph transformer for vision-and-language navigation,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16\,537--16\,547

    work page 2022

  5. [5]

    G. Zhou, Y. Hong, and Q. Wu, ``Navgpt: Explicit reasoning in vision-and-language navigation with large language models,'' in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 7, 2024, pp. 7641--7649

    work page 2024

  6. [6]

    Krantz, E

    J. Krantz, E. Wijmans, A. Majumdar, D. Batra, and S. Lee, ``Beyond the nav-graph: Vision-and-language navigation in continuous environments,'' in Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXVIII 16. 1em plus 0.5em minus 0.4em Springer, 2020, pp. 104--120

    work page 2020

  7. [7]

    Krantz and S

    J. Krantz and S. Lee, ``Sim-2-sim transfer for vision-and-language navigation in continuous environments,'' in European conference on computer vision. 1em plus 0.5em minus 0.4em Springer, 2022, pp. 588--603

    work page 2022

  8. [8]

    X. Shi, Z. Li, W. Lyu, J. Xia, F. Dayoub, Y. Qiao, and Q. Wu, ``Smartway: Enhanced waypoint prediction and backtracking for zero-shot vision-and-language navigation,'' arXiv preprint arXiv:2503.10069, 2025

    arXiv 2025

Show all 50 references
  1. [9]

    Y. Hong, Z. Wang, Q. Wu, and S. Gould, ``Bridging the gap between learning in discrete and continuous environments for vision-and-language navigation,'' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 15\,439--15\,449

    2022

  2. [10]

    Z. Wang, X. Li, J. Yang, Y. Liu, and S. Jiang, ``Gridmm: Grid memory map for vision-and-language navigation,'' in Proceedings of the IEEE/CVF International conference on computer vision, 2023, pp. 15\,625--15\,636

    2023

  3. [11]

    Z. Wang, X. Li, J. Yang, Y. Liu, J. Hu, M. Jiang, and S. Jiang, ``Lookahead exploration with neural radiance representation for continuous vision-language navigation,'' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 13\,753--13\,762

    2024

  4. [12]

    G. Zhou, Y. Hong, Z. Wang, X. E. Wang, and Q. Wu, ``Navgpt-2: Unleashing navigational reasoning capability for large vision-language models,'' in European Conference on Computer Vision. 1em plus 0.5em minus 0.4em Springer, 2024, pp. 260--278

    2024

  5. [13]

    Zhang and P

    Y. Zhang and P. Kordjamshidi, ``Narrowing the gap between vision and action in navigation,'' in Proceedings of the 32nd ACM International Conference on Multimedia, 2024, pp. 856--865

    2024

  6. [14]

    Z. Wang, J. Li, Y. Hong, Y. Wang, Q. Wu, M. Bansal, S. Gould, H. Tan, and Y. Qiao, ``Scaling data generation in vision-and-language navigation,'' in Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 12\,009--12\,020

    2023

  7. [15]

    X. Li, Z. Wang, J. Yang, Y. Wang, and S. Jiang, ``Kerm: Knowledge enhanced reasoning for vision-and-language navigation,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2583--2592

    2023

  8. [16]

    Krantz, A

    J. Krantz, A. Gokaslan, D. Batra, S. Lee, and O. Maksymets, ``Waypoint models for instruction-guided navigation in continuous environments,'' in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 15\,162--15\,171

    2021

  9. [17]

    P. Chen, D. Ji, K. Lin, R. Zeng, T. Li, M. Tan, and C. Gan, ``Weakly-supervised multi-granularity map learning for vision-and-language navigation,'' Advances in Neural Information Processing Systems, vol. 35, pp. 38\,149--38\,161, 2022

    2022

  10. [18]

    Zhang, K

    J. Zhang, K. Wang, R. Xu, G. Zhou, Y. Hong, X. Fang, Q. Wu, Z. Zhang, and H. Wang, ``Navid: Video-based vlm plans the next step for vision-and-language navigation,'' arXiv preprint arXiv:2402.15852, 2024

    2024 arXiv

  11. [19]

    C. Chi, Z. Xu, S. Feng, E. Cousineau, Y. Du, B. Burchfiel, R. Tedrake, and S. Song, ``Diffusion policy: Visuomotor policy learning via action diffusion,'' The International Journal of Robotics Research, p. 02783649241273668, 2023

    2023

  12. [20]

    S. Ross, G. Gordon, and D. Bagnell, ``A reduction of imitation learning and structured prediction to no-regret online learning,'' in Proceedings of the fourteenth international conference on artificial intelligence and statistics. 1em plus 0.5em minus 0.4em JMLR Workshop and C...

    2011

  13. [21]

    Zhang, Y

    W. Zhang, Y. Li, Y. Qiao, S. Huang, J. Liu, F. Dayoub, X. Ma, and L. Liu, ``Effective tuning strategies for generalist robot manipulation policies,'' arXiv preprint arXiv:2410.01220, 2024

    2024 arXiv

  14. [22]

    D. An, H. Wang, W. Wang, Z. Wang, Y. Huang, K. He, and L. Wang, ``Etpnav: Evolving topological planning for vision-language navigation in continuous environments,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    2024

  15. [23]

    D. An, Y. Qi, Y. Li, Y. Huang, L. Wang, T. Tan, and J. Shao, ``Bevbert: Multimodal map pre-training for language-guided navigation,'' Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

    2023

  16. [24]

    Lee and Y.-L

    S.-W. Lee and Y.-L. Kuo, ``Diff- DA gger: Uncertainty estimation with diffusion policy for robotic manipulation,'' in CoRL 2024 Workshop on Mastering Robot Manipulation in a World of Abundant Data, 2024. [Online]. Available: https://openreview.net/forum?id=dwFzx74tly

    2024

  17. [25]

    V. Jain, G. Magalhaes, A. Ku, A. Vaswani, E. Ie, and J. Baldridge, `` Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation ,'' in Proc. of ACL, 2019

    2019

  18. [26]

    Deitke, D

    M. Deitke, D. Batra, Y. Bisk, T. Campari, A. X. Chang, D. S. Chaplot, C. Chen, C. P. D'Arpino, K. Ehsani, A. Farhadi et al., ``Retrospectives on the embodied ai workshop,'' arXiv preprint arXiv:2210.06849, 2022

    2022 arXiv

  19. [27]

    C.-Y. Ma, J. Lu, Z. Wu, G. AlRegib, Z. Kira, R. Socher, and C. Xiong, ``Self-monitoring navigation agent via auxiliary progress estimation,'' arXiv preprint arXiv:1901.03035, 2019

    1901 arXiv

  20. [28]

    S. Wang, C. Montgomery, J. Orbay, V. Birodkar, A. Faust, I. Gur, N. Jaques, A. Waters, J. Baldridge, and P. Anderson, ``Less is more: Generating grounded navigation instructions from landmarks,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit...

    2022

  21. [29]

    Touvron, T

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi \`e re, N. Goyal, E. Hambro, F. Azhar et al., ``Llama: Open and efficient foundation language models,'' arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  22. [30]

    H. Liu, C. Li, Q. Wu, and Y. J. Lee, ``Visual instruction tuning,'' Advances in neural information processing systems, vol. 36, pp. 34\,892--34\,916, 2023

    2023

  23. [31]

    J. Chen, B. Lin, R. Xu, Z. Chai, X. Liang, and K.-Y. K. Wong, ``Mapgpt: Map-guided prompting with adaptive path planning for vision-and-language navigation,'' arXiv preprint arXiv:2401.07314, 2024

    2024 arXiv

  24. [32]

    Kolve, R

    E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, M. Deitke, K. Ehsani, D. Gordon, Y. Zhu et al., ``Ai2-thor: An interactive 3d environment for visual ai,'' arXiv preprint arXiv:1712.05474, 2017

    2017 arXiv

  25. [33]

    F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese, ``Gibson env: Real-world perception for embodied agents,'' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 9068--9079

    2018

  26. [34]

    Savva, A

    M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, D. Parikh, and D. Batra, ``Habitat: A P latform for E mbodied AI R esearch,'' in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019

    2019

  27. [35]

    Y. Hong, Q. Wu, Y. Qi, C. Rodriguez-Opazo, and S. Gould, ``A recurrent vision-and-language bert for navigation,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 1643--1653

    2021

  28. [36]

    B. Lin, Y. Nie, Z. Wei, Y. Zhu, H. Xu, S. Ma, J. Liu, and X. Liang, ``Correctable landmark discovery via large models for vision-language navigation,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

    2024

  29. [37]

    J. Chen, B. Lin, X. Liu, L. Ma, X. Liang, and K.-Y. K. Wong, ``Affordances-oriented planning using foundation models for continuous vision-language navigation,'' in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 22, 2025, pp. 23\,568--23\,576

    2025

  30. [38]

    T. Ren, S. Liu, A. Zeng, J. Lin, K. Li, H. Cao, J. Chen, X. Huang, Y. Chen, F. Yan et al., ``Grounded sam: Assembling open-world models for diverse visual tasks,'' arXiv preprint arXiv:2401.14159, 2024

    2024 arXiv

  31. [39]

    Rombach, A

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, ``High-resolution image synthesis with latent diffusion models,'' in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10\,684--10\,695

    2022

  32. [40]

    X. Ma, S. Patidar, I. Haughton, and S. James, ``Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 18\,081--18\,090

    2024

  33. [41]

    C. Hao, K. Lin, S. Luo, and H. Soh, ``Language-guided manipulation with diffusion policies and constrained inpainting,'' arXiv preprint arXiv:2406.09767, 2024

    2024 arXiv

  34. [42]

    Sridhar, D

    A. Sridhar, D. Shah, C. Glossop, and S. Levine, ``Nomad: Goal masked diffusion policies for navigation and exploration,'' in 2024 IEEE International Conference on Robotics and Automation (ICRA). 1em plus 0.5em minus 0.4em IEEE, 2024, pp. 63--70

    2024

  35. [43]

    W. Yu, J. Peng, H. Yang, J. Zhang, Y. Duan, J. Ji, and Y. Zhang, ``Ldp: A local diffusion planner for efficient robot navigation and collision avoidance,'' in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1em plus 0.5em minus 0.4em IEEE, 2024...

    2024

  36. [44]

    Zhang, H

    G. Zhang, H. Tang, and Y. Yan, ``Versatile navigation under partial observability via value-guided diffusion policy,'' in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 17\,943--17\,951

    2024

  37. [45]

    Chang, A

    A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, ``Matterport3d: Learning from rgb-d data in indoor environments,'' arXiv preprint arXiv:1709.06158, 2017

    2017 arXiv

  38. [46]

    Wijmans, A

    E. Wijmans, A. Kadian, A. Morcos, S. Lee, I. Essa, D. Parikh, M. Savva, and D. Batra, ``Dd-ppo: Learning near-perfect pointgoal navigators from 2.5 billion frames,'' in International Conference on Learning Representations, 2020. [Online]. Available: https://openreview.net/foru...

    2020

  39. [47]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., ``Learning transferable visual models from natural language supervision,'' in International conference on machine learning. 1em plus 0.5em minus 0.4em PmLR, 202...

    2021

  40. [48]

    K. He, X. Zhang, S. Ren, and J. Sun, ``Deep residual learning for image recognition,'' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770--778

    2016

  41. [49]

    Tan and M

    H. Tan and M. Bansal, ``Lxmert: Learning cross-modality encoder representations from transformers,'' arXiv preprint arXiv:1908.07490, 2019

    1908 arXiv

  42. [50]

    A. Q. Nichol and P. Dhariwal, ``Improved denoising diffusion probabilistic models,'' in International conference on machine learning. 1em plus 0.5em minus 0.4em PMLR, 2021, pp. 8162--8171

    2021

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.