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arxiv: 2605.31066 · v1 · pith:AJEW2PKFnew · submitted 2026-05-29 · 💻 cs.RO

Can Aerial VLA Models Cooperate? Evaluating Closed-Loop Air-Ground Coordination with CARLA-Air

Tianle Zeng , Yanci Wen , Xueang Yu , Hong Zhang This is my paper

Pith reviewed 2026-06-28 22:20 UTC · model grok-4.3

classification 💻 cs.RO
keywords aerial VLAair-ground coordinationCARLA-AirUAV-UGV cooperationclosed-loop evaluationvision-language-action modelsmulti-agent simulation
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The pith

Aerial VLA models can track ground partners but fail to turn that into stable cooperative behavior in closed-loop tasks.

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

The paper presents CARLA-Air, a unified simulation that combines CARLA and AirSim in one runtime so a UAV and UGV share the same world state, physics, and sensors. It runs two diagnostic tasks—moving-platform landing and occlusion-recovery escort—to check whether single-agent aerial VLA skills transfer to joint air-ground action. Results indicate that models often follow or track the ground partner yet cannot sustain coordinated team performance. Simple state prompts add little help, and basic bidirectional text exchange frequently worsens outcomes by spreading errors. The work concludes that zero-shot cooperation under current text interfaces needs explicit partner-state grounding, low-latency action links, and shared team objectives.

Core claim

Current aerial VLA models can often track or follow a ground partner, but struggle to convert this single-agent competence into stable cooperative behavior. State prompting provides limited benefit, and naive bidirectional interaction fails to consistently improve performance and can amplify errors for most baselines. These findings suggest that, under the tested text-based cue interfaces, zero-shot cooperative air-ground VLA requires three components beyond the current paradigm: explicit partner-state grounding, low-latency action coordination, and team-level objective alignment.

What carries the argument

CARLA-Air, a single-process environment that unifies CARLA and AirSim to share world state, physics tick, and sensing pipeline for consistent UAV-UGV interaction and precise latency measurement.

If this is right

  • Single-agent tracking skills do not automatically produce stable joint air-ground behavior.
  • Text-based state prompting yields only marginal gains in coordination tasks.
  • Naive bidirectional text interaction can increase error rates rather than reduce them.
  • Zero-shot cooperation needs explicit partner-state grounding, low-latency coordination, and team objective alignment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same interface limitations may appear in other multi-robot settings that rely on language for coordination.
  • Adding direct state sharing or visual grounding between agents could be tested as a direct extension of the current diagnostic tasks.
  • Hardware experiments on physical UAV-UGV pairs would be needed to check whether the observed cooperation gaps persist outside simulation.

Load-bearing premise

The two diagnostic tasks and the text-based cue interfaces used in CARLA-Air represent the coordination challenges that would appear in real air-ground deployments or with richer interaction modalities.

What would settle it

An experiment in which the same VLA baselines, given direct partner-state access and low-latency channels instead of text cues, achieve stable success on both the landing and escort tasks would falsify the claim that the current paradigm is insufficient.

Figures

Figures reproduced from arXiv: 2605.31066 by Hong Zhang, Tianle Zeng, Xueang Yu, Yanci Wen.

Figure 1
Figure 1. Figure 1: CARLA-AIR runtime architecture. CARLA and AirSim are embedded in one engine￾level runtime with a shared world state, physics tick, and sensor/rendering pipeline. Native CARLA and AirSim APIs are preserved, while both command streams are resolved inside the same runtime [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Landing process trajectory diagnosis. Top: representative visual sequence during moving-platform landing. Bottom: 3D UGV and UAV trajectories during moving-platform land￾ing under (a) C0, (b) C1, and (c) C2. Colored curves denote aerial baselines; gray line denotes the UGV trajectory; black dashed line denotes Rule-Coop-State. 4 Diagnostic Evaluation Building on the physically consistent and closed-loop ev… view at source ↗
Figure 3
Figure 3. Figure 3: Cooperative Occlusion-Recovery Escort. The UAV escorts the UGV, loses visual con￾tact under temporary occlusion, searches using partner-state cues, and re-acquires the UGV before resuming escort. For landing, we report Tracking Success Rate (TSR) as the single-UAV primitive score and Landing Success Rate (LSR) as the final cooperative task score. We define Cooperative Conversion Rate as CCR = LSR/ max(TSR,… view at source ↗
Figure 4
Figure 4. Figure 4: illustrates the core integration mechanism: CARLA-AIR resolves the single-GameMode constraint by keeping CARLA as the authoritative world manager and composing the AirSim aerial subsystem as an actor-level component. A.1 Coordinate Frame Unification and Single-Tick Execution All cross-agent states, observations, and cooperation metrics are expressed in a unified metric frame. CARLA uses a left-handed Unrea… view at source ↗
Figure 5
Figure 5. Figure 5: Coordinate-frame alignment. CARLA uses the Unreal Engine frame in centimetres with Z-up, while AirSim uses a metre-scale NED frame with Z-down. CARLA-AIR applies a deterministic scale conversion and Z-axis sign flip to express UAV and UGV states in one metric frame [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of synchronized aerial-ground sensing. Vehicle-side and UAV-side sensor streams are sampled from the same simulation tick in CARLA-AIR. The top row shows vehicle-side modalities and the bottom row shows UAV-side modalities, including RGB, semantic segmentation, depth, LiDAR BEV, surface normals, and instance segmentation. This provides paired multi-modal observations without cross-process timestamp… view at source ↗
read the original abstract

Recent aerial vision-language-action (VLA) models show promising single-UAV capabilities, such as tracking moving objects and navigating to language-specified landmarks. However, it remains unclear whether these capabilities can transfer to air-ground cooperation, where a UAV and a UGV must act jointly in a shared, closed-loop physical world. We study this question with CARLA-Air, a single-process air-ground evaluation environment that unifies CARLA and AirSim inside one Unreal Engine runtime. By sharing the same world state, physics tick, and sensing pipeline, CARLA-Air enables physically consistent UAV--UGV interaction and precise measurement of simulation-timestamp alignment and effective coordination latency. Using CARLA-Air, we evaluate representative aerial VLA and planning baselines on two complementary diagnostic tasks: moving-platform landing and occlusion-recovery escort. The results show that current aerial VLA models can often track or follow a ground partner, but struggle to convert this single-agent competence into stable cooperative behavior. State prompting provides limited benefit, and naive bidirectional interaction fails to consistently improve performance and can amplify errors for most baselines. These findings suggest that, under the tested text-based cue interfaces, zero-shot cooperative air-ground VLA requires three components beyond the current paradigm: explicit partner-state grounding, low-latency action coordination, and team-level objective alignment. Our code is available at https://github.com/louiszengCN/CarlaAir.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The paper introduces CARLA-Air, a single-process simulation environment unifying CARLA and AirSim to enable physically consistent closed-loop evaluation of air-ground coordination. It evaluates representative aerial VLA and planning baselines on two diagnostic tasks—moving-platform landing and occlusion-recovery escort—finding that single-agent tracking succeeds but stable cooperative behavior does not. State prompting yields limited benefit, while naive bidirectional interaction often fails to improve performance and can amplify errors. The authors conclude that, under the tested text-based cue interfaces, zero-shot cooperative air-ground VLA requires explicit partner-state grounding, low-latency action coordination, and team-level objective alignment. The code is released at https://github.com/louiszengCN/CarlaAir.

Significance. If the empirical results hold under the stated scoping, the work is significant for providing the first unified closed-loop benchmark for aerial VLA cooperation with ground agents. The shared world state, physics tick, and sensing pipeline enable precise latency and alignment measurements, which is a clear methodological strength. The open-source release supports reproducibility. The findings identify concrete gaps in current VLA paradigms for multi-agent settings and suggest targeted requirements for future work.

minor comments (3)
  1. Abstract: The term 'naive bidirectional interaction' is used without a brief definition or example of the prompting format, which could reduce clarity for readers unfamiliar with the exact interface implementation.
  2. The manuscript should include a short paragraph in the methods or appendix summarizing the number of trials, random seeds, and variance reporting for the performance metrics to aid verification of the reported differences.
  3. Figure captions (e.g., those showing task trajectories or performance bars) would benefit from explicit mention of the metrics plotted and any statistical annotations used.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, significance assessment, and recommendation of minor revision. No major comments appear in the provided report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

This paper is an empirical evaluation study that introduces the CARLA-Air simulator and reports performance measurements of existing VLA models on two diagnostic tasks. The abstract and provided text contain no derivation chain, equations, fitted parameters presented as predictions, or load-bearing self-citations. Central claims rest on experimental results obtained in the new environment rather than any reduction to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical evaluation paper; no mathematical derivations, fitted parameters, or new physical entities are introduced. The central claims rest on the representativeness of the chosen tasks and interfaces rather than on axioms or free parameters.

pith-pipeline@v0.9.1-grok · 5792 in / 1119 out tokens · 16552 ms · 2026-06-28T22:20:43.575859+00:00 · methodology

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Reference graph

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