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REVIEW 4 major objections 5 minor 40 references

Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read SPINE, a closed-loop LLM planner, completed 12 of 14 kilometer-scale field missions on ground robots, and a distilled 3-billion-parameter model planned UAV flights entirely onboard.

desk verdict A genuinely first on-device LM UAV planner, but the headline 12/14 UGV success count includes two manual takeovers and is softer than it reads. read the letter →

arxiv 2505.09477 v1 pith:53EQXWHN submitted 2025-05-14 cs.RO cs.AI

Zachary Ravichandran , Fernando Cladera , Jason Hughes , Varun Murali , M. Ani Hsieh , George J. Pappas , Camillo J. Taylor , Vijay Kumar This is my paper · ORCID
classification cs.ROcs.AI
keywords foundationmodelslargelanguagefieldroboticssemanticplanningmodeldistillationUAVUGVnavigationclosed-loopvalidation
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

This paper reports field deployments of SPINE, a closed-loop autonomy framework in which a large language model turns an incomplete natural-language mission into a sequence of robot behaviors. Across urban, semi-urban, and rural sites, the ground version completed twelve of fourteen missions, with failures attributed to communication loss and odometry drift; two missions involving obstacle-detection failures were completed only after a brief manual takeover. The paper also reports preliminary distillation results: a LoRA-fine-tuned Llama-3.2-3B model running on a UAV's onboard computer planned three of four short missions correctly on the first try, which the authors present as the first language-driven UAV planner with fully onboard computation. The underlying argument is that embedding foundation models in a validated closed loop, rather than generating plans from a known map, is what makes language-specified missions work in large, unstructured, partially known terrain. If the demonstration holds, foundation-model-enabled robots can move from indoor, map-known settings toward useful field autonomy.

What carries the argument

The central object is SPINE, a closed-loop LLM-enabled planner built from plan generation and plan validation. Plan generation uses an LLM to turn the user's natural-language mission into a task sequence expressed in the robot's behavior API, using chain-of-thought reasoning and receiving incremental map updates as text. Plan validation checks each proposed behavior against syntax, reachability, and explorability constraints and returns natural-language error feedback. Around this planner sits the field-deployment machinery: a semantic graph, continuously built and shared between ground and aerial platforms; the ground stack's LiDAR odometry and ground segmentation; the aerial stack's waypoint selection; and LoRA distillation, which fine-tunes a small Llama model on expert planner demonstrations so it can run onboard a compute-limited UAV.

What would settle it

Rerun the fourteen UGV missions with the same specifications and map conditions but forbid any manual takeover, and separately rerun the four UAV missions with no prior semantic map and no server connectivity; if the first-try success rates fall materially from 12/14 and 3/4, the demonstration claims would not survive.

Watch

Extended reading notes

Core claim

The central claim is that language-driven planning by foundation models can be moved from closed-world settings into large-scale unstructured field environments, provided the language model is embedded in a closed loop with a semantic mapper and a plan validator. The paper reports that SPINE, using GPT-4o as the planner, completed 12 of 14 UGV missions requiring 2–8 reasoning steps and traversals from 100 m to 1 km, with two failures from communication loss and odometry drift and two obstacle-detection failures resolved by brief manual takeover. It further claims the first language-driven UAV planner running entirely on-device: a LoRA-distilled Llama-3.2-3B model onboard a Falcon 4 UAV fulfilled three of four short missions on the first try, and in an offline planning benchmark the distilled model outscored an off-the-shelf 3B model (72.7% versus 9.2%) while still trailing GPT-4o (100%). The paper also documents that online plan validation is load-bearing: without it, mission success drops sharply as the unknown portion of the map grows.

Load-bearing premise

The reported 12-of-14 success rate counts missions finished after a brief manual takeover during obstacle-detection failures as successes, so the strength of the field demonstration depends on accepting that intervention as still a success.

Editorial extensions

If this is right

  • Language-specified missions can be run at kilometer scales in unknown terrain when the planner is coupled to online validation and a continually updated semantic map.
  • On-device distilled models can replace server-based LLMs for short-horizon planning, removing the need for continuous connectivity in network-denied field settings.
  • The value of validation feedback is measurable: even minimal explanations of infeasibility substantially improve LLM planning success, so reliability depends on both the model and the validator.
  • Distilled small models retain useful planning ability (72.7% success) but not full parity with the expert (100%), making multi-iteration planning and complex reasoning the next performance bottleneck.
  • A shared hierarchical semantic graph can serve as the common representation that lets a UAV build mission-relevant maps and a UGV execute language-specified missions from them.

Reading between the lines

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

  • If the two missions completed after a manual takeover are counted as non-autonomous, the reported 12/14 completion rate becomes 10/14; publishing both counts would make future field comparisons cleaner.
  • The distilled planner's north-versus-south parking-lot confusion suggests a spatial-grounding deficit; a testable extension is to inject coordinates or topological pointers into the model's prompt.
  • The distillation setup hints at a compute-aware division of labor, where a large model decomposes missions and small onboard models execute well-specified subtasks, an architecture the paper sketches but does not test.
  • Because visual foundation models mislabel aerial images (cars as construction vehicles), fine-tuning on aerial viewpoints is a natural next experiment before visual-language planning can run onboard.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. This paper reports field deployments of SPINE, an LLM-based planning framework, on ground robots and a UAV in large-scale unstructured environments. The UGV portion summarizes missions from prior work by the same authors, reporting 12/14 missions completed in urban, semi-urban, and rural sites. The UAV portion presents preliminary distillation of Llama-3.2-3B using GPT-4o-generated planning data, with 3/4 short missions fulfilled on the first try and a planning comparison on 11 specifications among GPT-4o, distilled Llama-3.2-3B, and off-the-shelf Llama-3.2-3B. The paper claims the first demonstration of kilometer-scale LLM-enabled robot planning in unstructured environments and the first language-driven UAV planner using on-device language models, and it concludes with lessons learned and open challenges.

Significance. If the reported results are taken at face value, the kilometer-scale UGV deployments are a useful step toward FM-enabled autonomy outside prior-map, closed-world settings, and the distillation result is a promising proof of concept for SWaP-limited onboard planning. The paper is honest about failure modes (communication loss, odometry drift, obstacle detection) and explicitly discloses the manual takeovers in S7 and S8. The headline claims are nonetheless moderated by very small sample sizes, the conflation of autonomous and human-assisted successes, and the fact that the main UGV evidence is inherited from self-cited prior work rather than re-derived or fully described here. The negative result for off-the-shelf Llama-3.2-3B (9.2%) is a useful data point for the community, and the lessons-learned section is practical and appropriately cautious.

major comments (4)
  1. [III-A and Table I] Table I lists S7 and S8 as successful outcomes with "Obst. det." as the failure mode, and Section III-A states that "SPINE was able to complete the mission after brief manual takeover." Because the abstract and the first contribution bullet define the headline result by the 12/14 success count, counting these two runs as unqualified successes conflates human-assisted completion with autonomous completion. The paper should report both the strict autonomy rate (10/14 if S7 and S8 are excluded) and the assisted-completion rate, and should state the operational definition of success used for each specification.
  2. [III-B and Table II] The comparison among GPT-4o (100%), distilled Llama-3.2-3B (72.7%), and off-the-shelf Llama-3.2-3B (9.2%) is based on eleven specifications with no trial-by-trial listing, no confidence intervals, and no repeated sampling. With n=11, the 8/11 versus 11/11 difference is not statistically robust, so the unqualified claim that distillation "leads to a significant performance gain" is stronger than the evidence supports. The authors should provide per-specification results, repeated trials or a statistical test, and should soften the wording to something like "a promising improvement in this preliminary set."
  3. [III-B, UAV missions] The UAV evaluation covers only four short missions, and the third mission (construction in the northern parking lot) required a follow-up user command after the planner confused southern and northern parking lots. Presenting this as 3/4 "first try" and claiming "the first language-driven UAV planner using on-device language models" goes beyond what four missions can establish. The authors should define "first try" explicitly, report the corrected-outcome rate separately, or restrict the novelty claim to the specific distillation-plus-onboard-execution configuration that was demonstrated.
  4. [III-A and I] The paper's central "first demonstration" claim rests on UGV deployments that are summarized from [4] and [18] rather than fully described in this manuscript. Because the reader cannot verify trial conditions, intervention criteria, or the exact definition of success from the text alone, the authors should either include the trial protocol and per-trial data in an appendix or explicitly reframe this paper as a summary of prior UGV work whose new contribution is limited to the UAV distillation results.
minor comments (5)
  1. [III-B and Fig. 7] The text and Table II refer to Llama-3.2-3B, but the Fig. 7 captions refer to Llama 3.1 8B; please reconcile the model names and sizes.
  2. [Abstract] The abstract contains a typo: "FM-enabled robots primary operate" should read "primarily operate."
  3. [III-A and Table I] Specification S1 reports an outcome of 1/3 with an average distance of 1200 m; please clarify whether repeated attempts of the same specification are counted as separate trials and whether the distance is averaged over all attempts or only successful runs.
  4. [III-A and Fig. 5] Fig. 5 is reproduced from [4], but this paper does not provide the experimental protocol (number of runs, prompt configuration, definition of "unknown portion of map") for the no-validation versus with-validation comparison; please summarize the protocol or clearly mark the figure as prior-work data.
  5. [II-B] The phrase "based on air router, first introduced in [22]" should be written as "based on Air Router, first introduced in [22]" for consistency with the system name.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found; the paper is a field-experience summary whose main results are explicitly inherited from prior self-cited work, and the distillation evaluation is a standard teacher-student comparison rather than a disguised reuse of its inputs.

full rationale

This paper does not present a derivation chain in the sense of equations or predictions whose outputs are equivalent to their inputs by construction. The UGV mission results in Table I are explicitly reported as coming from prior papers by the same authors ('TABLE I UGV AUTONOMY OUTCOMES (FROM [18] AND [4])'), and the text repeatedly refers the reader to [4] and [18] for details of SPINE and the field deployments. This is self-citation, but it is not circular: the cited prior work reports externally conducted field experiments with concrete mission specifications, outcomes, and failure modes, and the current paper is a summary/position paper rather than an attempt to re-derive those results from assumptions. The distillation section is also not circular: the distilled Llama-3.2-3B model is trained on GPT-4o expert outputs, and then evaluated on new missions and on eleven specifications with semantic graphs from previous missions. That is the standard definition of distillation; the paper openly reports the performance gap (72.7% vs. 100%) and discloses limitations such as inability to generate long-horizon planning data. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in through citation. The count of '12/14' missions including two completed after brief manual takeover is a correctness/metrics concern about how 'success' is defined, not a circularity of the derivation. Overall, the central claims are either inherited from identifiable prior experiments or presented as preliminary results with disclosed limitations, so the paper receives a low score reflecting only the minor, non-circular reliance on self-citations.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claims rest on domain assumptions about representation (semantic graph plus text interface), validation completeness, the transferability of distillation, and the reliability of field hardware and networking. None of these are formalized or given independent justification; the paper is a systems report.

assumptions (4)
  • domain assumption LLM chain-of-thought over a text-serialized semantic map is a sufficient representation for mission-level planning.
    The entire SPINE planner assumes natural-language reasoning on graph nodes can produce executable task sequences (Section II-A).
  • domain assumption The closed-loop validation layer can catch infeasible or unsafe LLM plans using syntax, reachability, and explorable constraints.
    The paper attributes improved success to validation (Fig. 5), assuming the constraint set is complete enough for safety.
  • ad hoc to paper LoRA fine-tuning of a 3B model on GPT-4o-generated data transfers enough planning capability for short-horizon missions.
    The distillation framework is introduced ad hoc in Section III-B; no theoretical or independent evidence is given that this transfer works beyond the presented 11-spec evaluation.
  • domain assumption GPS, LiDAR odometry, and mesh communication provide sufficiently reliable state and connectivity for mission execution.
    Failure modes are communication loss and odometry drift (Tab. I), so the demonstrations depend on these subsystems.

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Pith. "Pith review of Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities." pith.science (2026). https://pith.science/paper/53EQXWHN

@misc{pith2026250509477,
  author       = {Pith},
  title        = {Pith review of: Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/53EQXWHN}},
  note         = {Machine review of arXiv:2505.09477}
}
read the original abstract

The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments. However, existing FM-enabled robots primary operate in closed-world settings, where the robot is given a full prior map or has a full view of its workspace. This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments. To effectively accomplish these missions, robots must actively explore their environments, navigate obstacle-cluttered terrain, handle unexpected sensor inputs, and operate with compute constraints. We discuss recent deployments of SPINE, our LLM-enabled autonomy framework, in field robotic settings. To the best of our knowledge, we present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions. SPINE is agnostic to a particular LLM, which allows us to distill small language models capable of running onboard size, weight and power (SWaP) limited platforms. Via preliminary model distillation work, we then present the first language-driven UAV planner using on-device language models. We conclude our paper by proposing several promising directions for future research.

Figures

Figures reproduced from arXiv: 2505.09477 by the authors.

Figure 1
Figure 1. Example missions. Top: The UGV is given a language-specified mission in a partially-known environment, and it performs several steps of reasoning, planning, and active mapping in order to fulfill the user’s request. Bottom: Deployment of a distilled language model running onboard a UAV for planning. field robots operate in uncertain and unstructured environ￾ments, which limits the utility of current FM-enabled robot… view at source ↗
Figure 2
Figure 2. UGV/UAV autonomy overview. Given a language-specified mission, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Overview of the robotic platforms and sensors used in our [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Importance of validation. Even minimal feedback significantly [PITH_FULL_IMAGE:figures/full_fig_p004_5.png]
Figure 4
Figure 4. Figure 4: We consider three experimental environments: [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 7
Figure 7. Figure 7: Planning capabilities across language models. each model must plan [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]
Figure 8
Figure 8. Figure 8: Example of the shortcomings of a Grounded Detection model. Cars [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

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Reference graph

Works this paper leans on

40 extracted references · 21 canonical work pages

  1. [4]

    Spine: Online semantic planning for missions with incomplete natural language specifications in unstructured environments,

    Z. Ravichandran, V . Murali, M. Tzes, G. J. Pappas, and V . Kumar, “Spine: Online semantic planning for missions with incomplete natural language specifications in unstructured environments,” International Conference on Robotics and Automation (ICRA) , 2025. [Online]. Available: https://arxiv.org/abs/2410.03035

    arXiv 2025

  2. [18]

    Air-ground col- laboration for language-specified missions in unknown environments,

    F. Cladera, Z. Ravichandran, J. Hughes, V . Murali, C. Nieto-Granda, M. A. Hsieh, G. J. Pappas, C. J. Taylor, and V . Kumar, “Air-ground col- laboration for language-specified missions in unknown environments,” Transactions on Field Robots , 2025, in submission

    work page 2025

  3. [1]

    Sayplan: Grounding large language models using 3d scene graphs for scalable task planning,

    K. Rana, J. Haviland, S. Garg, J. Abou-Chakra, I. Reid, and N. Suenderhauf, “Sayplan: Grounding large language models using 3d scene graphs for scalable task planning,” in 7th Annual Conference on Robot Learning , 2023. [Online]. Available: https://openreview.net/forum?id=wMpOMO0Ss7a

    work page 2023

  4. [2]

    Driving everywhere with large language model policy adaptation,

    B. Li, Y . Wang, J. Mao, B. Ivanovic, S. Veer, K. Leung, and M. Pavone, “Driving everywhere with large language model policy adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    work page 2024

  5. [3]

    Deploying and evaluating llms to program service mobile robots,

    Z. Hu, F. Lucchetti, C. Schlesinger, Y . Saxena, A. Freeman, S. Modak, A. Guha, and J. Biswas, “Deploying and evaluating llms to program service mobile robots,” IEEE Robotics and Automation Letters , 2024

    work page 2024

  6. [5]

    Code as policies: Language model programs for embodied control,

    J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as policies: Language model programs for embodied control,” in arXiv preprint arXiv:2209.07753 , 2022

    arXiv 2022

  7. [6]

    Lang2ltl: Translating natural language commands to temporal robot task specification,

    J. X. Liu, Z. Yang, I. Idrees, S. Liang, B. Schornstein, S. Tellex, and A. Shah, “Lang2ltl: Translating natural language commands to temporal robot task specification,” in Conference on Robbot Learning (CoRL), 2023. [Online]. Available: https://arxiv.org/abs/2302.11649

    arXiv 2023

  8. [7]

    Language-grounded dynamic scene graphs for interactive object search with mobile manipulation,

    D. Honerkamp, M. B ¨uchner, F. Despinoy, T. Welschehold, and A. Val- ada, “Language-grounded dynamic scene graphs for interactive object search with mobile manipulation,” arXiv preprint arXiv:2403.08605 , 2024

    arXiv 2024

Show all 40 references
  1. [8]

    Rea- soning about the unseen for efficient outdoor object navigation,

    Q. Xie, T. Zhang, K. Xu, M. Johnson-Roberson, and Y . Bisk, “Rea- soning about the unseen for efficient outdoor object navigation,” 2023

    2023

  2. [9]

    Robohop: Segment-based topological map representation for open-world visual navigation,

    S. Garg, K. Rana, M. Hosseinzadeh, L. Mares, N. Suenderhauf, F. Dayoub, and I. Reid, “Robohop: Segment-based topological map representation for open-world visual navigation,” arXiv, 2023

    2023

  3. [10]

    Navigation with large language models: Semantic guesswork as a heuristic for planning,

    D. Shah, M. R. Equi, B. Osi ´nski, F. Xia, B. Ichter, and S. Levine, “Navigation with large language models: Semantic guesswork as a heuristic for planning,” in Proceedings of The 7th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, J. Tan, M. Tous...

  4. [11]

    Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action,

    D. Shah, B. Osi ´nski, b. ichter, and S. Levine, “Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action,” in Proceedings of The 6th Conference on Robot Learning , ser. Proceedings of Machine Learning Research, K. Liu, D. Kulic, and J. Ichnows...

    2023

  5. [12]

    Inner monologue: Embodied reasoning through planning with language models,

    W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y . Chebotar, P. Sermanet, N. Brown, T. Jack- son, L. Luu, S. Levine, K. Hausman, and B. Ichter, “Inner monologue: Embodied reasoning through planning with language models,” inarXiv pr...

    2022 arXiv

  6. [13]

    ViKiNG: Vision-Based Kilometer-Scale Nav- igation with Geographic Hints,

    D. Shah and S. Levine, “ViKiNG: Vision-Based Kilometer-Scale Nav- igation with Geographic Hints,” in Proceedings of Robotics: Science and Systems, 2022

    2022

  7. [14]

    Neusis: A composi- tional neuro-symbolic framework for autonomous perception, reason- ing, and planning in complex uav search missions,

    Z. Cai, C. R. Cardenas, K. Leo, C. Zhang, K. Backman, H. Li, B. Li, M. Ghorbanali, S. Datta, L. Qu et al. , “Neusis: A composi- tional neuro-symbolic framework for autonomous perception, reason- ing, and planning in complex uav search missions,” arXiv preprint arXiv:2409.10196, 2024

    2024 arXiv

  8. [15]

    Aerialvln: Vision-and-language navigation for uavs,

    S. Liu, H. Zhang, Y . Qi, P. Wang, Y . Zhang, and Q. Wu, “Aerialvln: Vision-and-language navigation for uavs,” in International Conference on Computer Vision (ICCV) , 2023

    2023

  9. [16]

    Landmark-rxr: Solving vision-and-language navigation with fine- grained alignment supervision,

    K. He, Y . Huang, Q. Wu, J. Yang, D. An, S. Sima, and L. Wang, “Landmark-rxr: Solving vision-and-language navigation with fine- grained alignment supervision,” Advances in Neural Information Pro- cessing Systems, vol. 34, pp. 652–663, 2021

    2021

  10. [17]

    Aerial vision-and-language navigation via semantic-topo-metric representa- tion guided llm reasoning,

    Y . Gao, Z. Wang, L. Jing, D. Wang, X. Li, and B. Zhao, “Aerial vision-and-language navigation via semantic-topo-metric representa- tion guided llm reasoning,” arXiv preprint arXiv:2410.08500 , 2024

    2024 arXiv

  11. [19]

    Faster- lio: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels,

    C. Bai, T. Xiao, Y . Chen, H. Wang, F. Zhang, and X. Gao, “Faster- lio: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4861–4868, 2022

    2022

  12. [20]

    Groundgrid: Lidar point cloud ground segmentation and terrain estimation,

    N. Steinke, D. Goehring, and R. Rojas, “Groundgrid: Lidar point cloud ground segmentation and terrain estimation,” IEEE Robotics and Automation Letters, vol. 9, no. 1, pp. 420–426, 2024

    2024

  13. [21]

    Evmapper: High altitude orthomapping with event cameras,

    F. Cladera, K. Chaney, M. A. Hsieh, C. J. Taylor, and V . Kumar, “Evmapper: High altitude orthomapping with event cameras,” arXiv preprint arXiv:2409.18120, 2024

    2024 arXiv

  14. [22]

    Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications,

    F. Cladera, Z. Ravichandran, I. D. Miller, M. Ani Hsieh, C. J. Taylor, and V . Kumar, “Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 2610– 2616

    2024

  15. [23]

    Lora: Low-rank adaptation of large language models,

    E. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv preprint arXiv:2106.09685, 2021

    2021 arXiv

  16. [25]

    Stronger together: Air-ground robotic collaboration using semantics,

    I. D. Miller, F. Cladera, T. Smith, C. J. Taylor, and V . Kumar, “Stronger together: Air-ground robotic collaboration using semantics,” IEEE Robotics and Automation Letters , vol. 7, no. 4, pp. 9643–9650, 2022

    2022

  17. [26]

    Grounded sam: Assembling open-world models for diverse visual tasks,

    T. Ren, S. Liu, A. Zeng, J. Lin, K. Li, H. Cao, J. Chen, X. Huang, Y . Chen, F. Yan, Z. Zeng, H. Zhang, F. Li, J. Yang, H. Li, Q. Jiang, and L. Zhang, “Grounded sam: Assembling open-world models for diverse visual tasks,” 2024

    2024

  18. [27]

    Opportunistic commu- nication in robot teams,

    D. Mox, K. Garg, A. Ribeiro, and V . Kumar, “Opportunistic commu- nication in robot teams,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 12 090–12 096

    2024

  19. [28]

    Evaluating real-world robot manipulation policies in simulation,

    X. Li, K. Hsu, J. Gu, K. Pertsch, O. Mees, H. R. Walke, C. Fu, I. Lunawat, I. Sieh, S. Kirmani, S. Levine, J. Wu, C. Finn, H. Su, Q. Vuong, and T. Xiao, “Evaluating real-world robot manipulation policies in simulation,” arXiv preprint arXiv:2405.05941 , 2024

    2024 arXiv

  20. [29]

    Open X-Embodiment: Robotic learning datasets and RT-X models,

    O. X.-E. Collaboration, A. O’Neill, A. Rehman, A. Gupta, A. Mad- dukuri, A. Gupta, . Z. Fu, and Z. Lin, “Open X-Embodiment: Robotic learning datasets and RT-X models,” https://arxiv.org/abs/2310.08864, 2023

    2023 arXiv

  21. [30]

    SANPO: A scene understanding, accessibil- ity, navigation, pathfinding, obstacle avoidance dataset,

    S. M. Waghmare, K. Wilber, D. Hawkey, M. Sirotenko, X. Yang, M. Wilson, S. Debats, C. Nuengsigkapian, A. Sharma, L. Pandikow, H. Wang, and H. Adam, “SANPO: A scene understanding, accessibil- ity, navigation, pathfinding, obstacle avoidance dataset,”arXiv preprint arXiv:2309.12...

    2024 arXiv

  22. [31]

    Gibson env: Real-world perception for embodied agents,

    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

  23. [32]

    Hm3d- ovon: A dataset and benchmark for open-vocabulary object goal navigation,

    N. Yokoyama, R. Ramrakhya, A. Das, D. Batra, and S. Ha, “Hm3d- ovon: A dataset and benchmark for open-vocabulary object goal navigation,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 5543–5550

    2024

  24. [33]

    Matterport3d: Learning from rgb-d data in indoor environments,

    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

  25. [34]

    Robothor: An open simulation-to-real embodied ai platform,

    M. Deitke, W. Han, A. Herrasti, A. Kembhavi, E. Kolve, R. Mottaghi, J. Salvador, D. Schwenk, E. VanderBilt, M. Wallingford et al. , “Robothor: An open simulation-to-real embodied ai platform,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition...

    2020

  26. [35]

    Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai,

    S. K. Ramakrishnan, A. Gokaslan, E. Wijmans, O. Maksymets, A. Clegg, J. Turner, E. Undersander, W. Galuba, A. Westbury, A. X. Chang et al., “Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai,” arXiv preprint arXiv:2109.08238 , 2021

    2021 arXiv

  27. [36]

    Large-scale autonomous flight with real-time semantic slam under dense forest canopy,

    X. Liu, G. V . Nardari, F. C. Ojeda, Y . Tao, A. Zhou, T. Donnelly, C. Qu, S. W. Chen, R. A. F. Romero, C. J. Taylor, and V . Kumar, “Large-scale autonomous flight with real-time semantic slam under dense forest canopy,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp...

    2022

  28. [37]

    Learning quadrupedal locomotion over challenging terrain,

    J. Lee, J. Hwangbo, L. Wellhausen, V . Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science Robotics, vol. 5, no. 47, p. eabc5986, 2020

    2020

  29. [38]

    Proprioception Is All You Need: Terrain Classification for Boreal Forests,

    D. LaRocque, W. Guimont-Martin, D.-A. Duclos, P. Gigu `ere, and F. Pomerleau, “Proprioception Is All You Need: Terrain Classification for Boreal Forests,” 2024, accepted

    2024

  30. [39]

    Nebula: Quest for robotic autonomy in challenging environments; team costar at the darpa subterranean challenge,

    S. Khattak, R. Hudjakov, D. Maturana, A. Ginting, H. Wang, D. Fan, A. Bouman, T. Touma, K. Ebadi, I. Abeywardena et al. , “Nebula: Quest for robotic autonomy in challenging environments; team costar at the darpa subterranean challenge,” in Field and Service Robotics . Springer...

    2021

  31. [229]

    2683–2699

    PMLR, 06–09 Nov 2023, pp. 2683–2699. [Online]. Available: https://proceedings.mlr.press/v229/shah23c.html

    2023

  32. [2024]

    Available: https://arxiv.org/abs/2407.21783

    [Online]. Available: https://arxiv.org/abs/2407.21783

    arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.