Like Liu, Zhengzheng Xu, Haitao He, Hongzhe Li, Shuchang Zhang, Dian Shao†

†Corresponding Author
Northwestern Polytechnical University, China University of Petroleum

If you like our project, please give us a star ⭐ on GitHub for latest update.

       

This is the official code package for UAV-DualCog.

UAV-DualCog benchmark overview. The benchmark organizes self-aware and environment-aware reasoning across image and video tasks, and the released code package supports the corresponding Stage 1-4 construction and evaluation workflow.

• Website: https://uav-dualcog.lozumi.com/
• Code repo: https://github.com/SmartDianLab/UAV-DualCog
• Dataset (ModelScope): https://www.modelscope.cn/datasets/Lozumi/UAV-DualCog
• Dataset (Hugging Face): https://huggingface.co/datasets/Lozumi/UAV-DualCog
• AerialVLN simulator: https://www.kaggle.com/datasets/shuboliu/aerialvln-simulators

For benchmark definitions, leaderboard interpretation, and detailed supplementary explanations, please read the website pages in order: Home -> Benchmark -> Construction -> Evaluation -> Leaderboard -> Analysis -> Usage.

• scripts/uav_dualcog/: Stage 1-4 entrypoints, pipeline orchestrator, shared utilities.
• trajectory/: hierarchical atomic/composite behavior library and composition logic.
• sim_bridge/: simulator abstraction and AirSim bridge.
• configs/uav_dualcog/: 18 runnable per-scene configs + shared configs + task-pipeline spec.
• configs/uav_dualcog/templates/: fully commented templates for customization.
• configs/prompts/uav_dualcog_prompts.yaml: Stage 2-4 prompt package.
• environment.yml, requirements.txt, deps/: environment/dependency references.

• No private keys or private endpoints.
• No generated artifacts (scene_data/, task_pipeline_data/, caches, media outputs).
• No internal notes/workflow docs outside public release scope.

Note:

• Environment setup records and Stage 1-4 empirical run logs in this package are provided under logs/.

UAV-DualCog/
├── scripts/uav_dualcog/                        # Stage 1-4 + task_pipeline entrypoints
├── trajectory/                                  # behavior elements/sets and composition
├── sim_bridge/                                  # AirSim bridge and engine adapter layer
├── configs/
│   ├── uav_dualcog/
│   │   ├── task_airsim_env_<id>.yaml           # runnable scene configs (18 scenes)
│   │   ├── common_stage_configs.yaml            # behavior library and shared stage defaults
│   │   ├── common_api_runtime.yaml              # model routing (API + local deployment)
│   │   ├── task_pipeline/
│   │   │   └── task_pipeline_uav_dualcog_v1.yaml
│   │   └── templates/                           # fully-commented config templates
│   └── prompts/
│       └── uav_dualcog_prompts.yaml
├── envs/                                        # simulator env assets (download separately)
│   └── airsim/
│       └── env_7/
├── scene_data/                                  # Stage 1-2 outputs
│   └── airsim_env_7/
│       ├── pcd_map/
│       ├── landmarks_raw/
│       └── landmarks_review/
├── task_pipeline_data/                          # Stage 3-4 outputs
│   └── UAV-DualCog-V1/
│       ├── airsim_env_7/
│       │   ├── video_tasks/
│       │   └── image_tasks/
│       └── task_pipeline/
│           ├── dataset_stats/
│           ├── exports/
│           └── landmark_lists/
├── environment.yml                              # conda environment reference
├── requirements.txt
└── deps/

Use this when reproducing benchmark construction from scene/simulator inputs.

Requires:

• envs/airsim/env_* simulator files.
• writeable scene_data/ and task_pipeline_data/.
• stage configs + prompt package.

Recommended workflow:

1. Stage 1 collects and fuses scene point clouds.
2. Stage 2 collects landmark candidates and performs review/auto-labeling.
3. Stage 3 generates behavior-driven video tasks.
4. Stage 4 generates image tasks and evaluation manifests.

Important operational notes:

• Stage 2 Step 2-4 are completed in the internal review web (review_instances_web + auto-label flow).
• Stage 3 and Stage 4 both provide internal web workbenches for inspection (behavior library, landmark/task previews, experiment outputs), but for released split generation we recommend task_pipeline.py batch phases.
• Some operations are available in both CLI and web forms. In practice, we recommend:
  • Stage 2: use the internal review web for Step 2-4 (screening, representative main-view confirmation, single-direction anchoring, auto-label review).
  • Stage 3 / Stage 4: use task_pipeline.py for released-scale batch generation, and use the internal web mainly for visual inspection, prompt/debug checks, and experiment/result browsing.

Use this when you only evaluate models on released benchmark assets.

Requires:

• downloaded scene_data/airsim_env_* release (for scene/landmark context).
• downloaded task_pipeline_data/UAV-DualCog-V1 release.
• no simulator environment files needed.
• common_api_runtime.yaml configured (API or local).

configs/uav_dualcog/common_api_runtime.yaml supports:

1. API routing (api_source: cloud/openrouter/...)
   Call remote OpenAI-compatible endpoints.
2. Local deployment (api_source: local)
   Call local OpenAI-compatible serving endpoints.
   The release package assumes local models are used as deployed, with no additional quantization handling logic in this code package.

Experiment model names can carry one runtime suffix:

• -Instant: force non-thinking style request controls where supported.
• -Thinking (or -Reasoning): force thinking/reasoning controls where supported.

Examples:

• Qwen/Qwen3.5-9B-Instant
• Qwen/Qwen3.5-9B-Thinking
• OpenGVLab/InternVL3_5-4B-Instant

Important behavior:

• The suffix is a request-mode switch, not a new routing key.
• Routing is resolved on the base model name in common_api_runtime.yaml (suffix stripped).
• Different providers/families expose different control knobs (enable_thinking, reasoning, chat_template_kwargs, etc.), and the runtime maps suffixes to family-compatible controls automatically.
• If a model family does not support a specific toggle, the runtime keeps a safe no-op behavior instead of rewriting benchmark semantics.

For vLLM environment setup, use the official quickstart:

• https://docs.vllm.com.cn/en/latest/getting_started/quickstart/#installation

Example model download (ModelScope):

modelscope download --model Qwen/Qwen3.5-4B --local_dir ./models/qwen3_5-4b

Example local serving command:

export CUDA_VISIBLE_DEVICES=3
export VLLM_USE_MODELSCOPE=true

vllm serve \
  ./models/internvl3_5-4b \
  --served-model-name OpenGVLab/InternVL3_5-4B-Instant \
  --tensor-parallel-size 1 \
  --reasoning-parser qwen3 \
  --max-model-len 32K \
  --kv-cache-dtype fp8 \
  --gpu-memory-utilization 0.90 \
  --max-num-seqs 8 \
  --max-num-batched-tokens 16K \
  --enable-prefix-caching \
  --host 0.0.0.0 \
  --port 40900

Recommended alignment:

• Keep --served-model-name consistent with the model alias used in experiments.
• Keep common_api_runtime.yaml -> api.models.<base_model>.request_model consistent with your served model name.

For safe dry checks (no real model calls), run:

python scripts/uav_dualcog/api_common.py --help
python scripts/uav_dualcog/mock_api_runtime_check.py --config configs/uav_dualcog/common_api_runtime.yaml

Fully commented templates are in:

• configs/uav_dualcog/templates/scene_config.template.yaml
• configs/uav_dualcog/templates/common_stage_configs.template.yaml
• configs/uav_dualcog/templates/common_api_runtime.template.yaml
• configs/uav_dualcog/templates/task_pipeline.template.yaml

Runnable examples are already provided under (env_7 shown here):

scene_id values are recommended to use the canonical env_<id> format throughout configs and commands. If --scene-id is passed on the command line, keep it identical to task.scene_id in the config; do not mix forms such as 7 and env_7 within one workspace.

• configs/uav_dualcog/task_airsim_env_7.yaml
• configs/uav_dualcog/common_stage_configs.yaml
• configs/uav_dualcog/common_api_runtime.yaml
• configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml

# configs/uav_dualcog/task_airsim_env_7.yaml
task:
  name: UAV-DualCog-env_7
  engine: airsim
  base_dir: scene_data
  scene_id: env_7
  scene_dir_name: airsim_env_7

output_layout:
  scene_dir_include_engine: true
  stage1_dir: pcd_map
  stage2_raw_dir: landmarks_raw
  stage2_review_dir: landmarks_review
  stage3_task_root_dir: video_tasks      # released Stage 3 root
  stage4_qa_dir: image_tasks             # released Stage 4 root

camera:
  width: 4096                            # source capture resolution (DCI 4K 4:3)
  height: 3072
  fov: 72.0                              # camera FoV in degrees
  fps: 10                                # source-frame sampling rate

collect:
  pose_settle_sec: 0.05                  # wait after pose set before capture

traj_map:
  VoxelWidth: 1.5                        # map voxel/grid size in meters
  LidarDelta: [30, 30, 50]               # LiDAR local sampling span (x,y,z) in meters
  MapBound: [-219, 191, -270, 268, -50, 52]

parallel:
  mode: single_instance_multi_thread     # one AirSim process + multi-thread collection
  workers: 6

stage2:
  collect_rgb_views_count: 8             # side views per landmark (top view controlled separately)
  collect_parallel_workers: 6
  collect_rgb_parallel_workers: 6
  collect_view_image_width: 4096
  collect_view_image_height: 3072

engine_params:
  airsim:
    sim_ip: 127.0.0.1
    sim_port: 41070
    launch_sim: true
    headless: true
    camera_name: front_0
    vehicle_name: drone_1
    lidar_range: 500.0                   # LiDAR max range (meters)
    lidar_points_per_second: 200000

# configs/uav_dualcog/common_stage_configs.yaml
stage3_behavior_library:
  shared:
    safety_distance_m: 2.0               # global safety clearance for trajectory generation
  elements:
    gradual_approach:
      display_name: Gradual Approach
      family: inspection
      camera_mode_default: landmark_track
      params:
        travel_distance_m: {min: 30, max: 120, default: 40, step: 10}
        descent_m: {min: 5, max: 40, default: 15, step: 5}
    circular_orbit:
      display_name: Circular Orbit
      family: orbit
      camera_mode_default: landmark_track
      params:
        extension_m: {min: 4, max: 36, default: 12, step: 2}
        arc_deg: {min: 45, max: 720, default: 180, step: 90}

# configs/uav_dualcog/common_api_runtime.yaml
api:
  default_models:
    stage2: Qwen/Qwen3.5-9B
    stage3: openai/gpt-5.3-chat
    stage4: Qwen/Qwen3.5-4B
  models:
    openai/gpt-5.3-chat:
      api_source: cloud
      api_base: ${UAV_DUALCOG_API_BASE}
      api_key: ${UAV_DUALCOG_API_KEY}
      request_model: gpt-5.3-chat
      rpm_limit: 60
      tpm_limit: 200000
    Qwen/Qwen3.5-9B:
      api_source: local
      api_base: http://127.0.0.1:28000/v1
      api_key: ${UAV_DUALCOG_LOCAL_API_KEY}

# configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml
task_name: UAV-DualCog-V1
task_pipeline_root_dir: task_pipeline_data
stage: both
phase: both
scene_ids: [env_7, env_8, env_9, env_10, env_11, env_13, env_16, env_17, env_20, env_21, env_23, env_24]
seed: 29

stage3:
  final_video_width: 1440                # released video resolution (1080P, 4:3)
  final_video_height: 1080
  final_capture_parallel_workers: 16
  record_parallel_workers: 16

stage4:
  env_capture_parallel_workers: 8
  overlay_parallel_workers: 32
  difficulties: [4way, 8way]

• Resolution (camera.width/height, collect_view_image_width/height): higher values improve fine-grained landmark geometry and bbox tightness, but increase capture latency, I/O pressure, and web rendering time.
• FoV (camera.fov): smaller FoV gives larger target scale but weaker context; larger FoV gives stronger context but more perspective distortion. 72 is the current balance used by release generation.
• LiDAR radius (engine_params.airsim.lidar_range, traj_map.LidarDelta): larger range/span increases scene coverage and far-object context, but increases point volume and fusion cost.
• Grid size (traj_map.VoxelWidth): smaller voxel width keeps finer geometry but increases memory/time; larger voxel width is faster but can blur small structures.
• Parallel collection (parallel.workers, stage2.collect_parallel_workers, stage2.collect_rgb_parallel_workers): thread-level parallelism improves throughput under one simulator process, but oversubscription can cause black frames or unstable capture timing.
• Frame rate (camera.fps, Stage-3 render fps settings): higher fps preserves motion detail and temporal labels better, but increases storage, transfer, and inference overhead.

The runnable defaults above are tuned for the current build machine:

• CPU: 2 x AMD EPYC 7452 (128 vCPUs total)
• RAM: 503 GiB
• GPU: 4 x NVIDIA GeForce RTX 4090 (24 GB each)

Important:

• In our tests, AirSim only runs reliably in a single process per scene pipeline.
• Therefore Stage 1/2 collection uses single-process, multi-thread capture (single_instance_multi_thread) instead of multi-process simulator launching.

Below uses env_7 as example scene.

conda env create -f environment.yml
conda activate uav-dualcog

If your server does not have a display device, you may need:

sudo apt install xdg-user-dirs xdg-utils
sudo apt install libegl1
sudo apt install vulkan-tools libvulkan1 mesa-vulkan-drivers

Environment setup records and Stage 1-4 empirical logs are available in:

logs/

Purpose: build segmented/fused scene cloud for landmark construction.

1.0 Probe and write back scene map bounds (recommended before large collection):

python scripts/uav_dualcog/probe_airsim_mapbound.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --workers 6 \
  --probe-source hybrid \
  --write-back \
  --output scene_data/airsim_env_7/pcd_map/mapbound_probe_env7.json

This step estimates robust traj_map.MapBound, EstimatedSurfaceZ, and related boundary fields for the current scene, then writes them back to the scene config for stable Stage 1 collection.

python scripts/uav_dualcog/stage1_collect_pcd.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode all \
  --engine airsim

Purpose: construct landmark instances and finalize reviewed semantic annotations.

2.1 Collect candidates and multiview evidence:

python scripts/uav_dualcog/stage2_landmark_label.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode collect_instances

2.2 Open review web (Step 2-4 are web-centered in practice):

python scripts/uav_dualcog/stage2_landmark_label.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode review_instances_web \
  --host 0.0.0.0 \
  --port 20261

2.3 Auto-label reviewed instances:

python scripts/uav_dualcog/stage2_landmark_label.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode auto_label

Purpose: generate missions/trajectories, render videos, build stage3 manifests.

Direct entrypoint:

python scripts/uav_dualcog/stage3_generate_traj.py --help

Recommended (batch/reproducible) pipeline phases:

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage3 --phase selection

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage3 --phase data

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage3 --phase render

Optional internal web workbench:

python scripts/uav_dualcog/stage3_generate_traj.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode web

Purpose: sample image QA tasks, render assets, export stage4 manifests.

Recommended pipeline phases:

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage4 --phase selection

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage4 --phase data

python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage4 --phase render

Optional internal web workbench:

python scripts/uav_dualcog/stage4_qa_generate_and_eval.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode web \
  --port 20264

The internal web tools are designed for interactive inspection, review, and run-level debugging. They are especially useful when you need to confirm whether geometry, views, prompts, task rows, or model outputs are qualitatively correct before launching large batches.

Recommended split:

• Stage 2 web is the primary interface for Step 2-4 landmark review and semantic finalization.
• Stage 3 / Stage 4 web are best treated as workbenches for visual validation, targeted reruns, and experiment/result browsing.
• Released-scale generation should still be driven by task_pipeline.py so selection, data export, render, and experiment phases remain reproducible.

Launch:

python scripts/uav_dualcog/stage2_landmark_label.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode review_instances_web \
  --host 0.0.0.0 \
  --port 20261

This web is the operational center of Stage 2 Step 2-4. It combines:

• left-side landmark list and progress counters,
• top-down point-cloud and RGB evidence panes,
• the eight-view RGB direction ring,
• auto-label fields,
• final semantic review fields.

Typical usage order:

1. Step 2 initial screening
   Use Drop immediately for unstable or clearly unusable candidates. Use Keep only after the landmark has a confirmed main view and one confirmed landmark-centric direction anchor.
2. Main-view confirmation
   In the eight-view RGB panel, select the most representative RGB view as the main view. The main view does not need to be front; it is simply the clearest and most benchmark-facing reference image for that landmark.
3. Single-direction confirmation
   Reviewers only need to assign one correct direction anchor for the chosen main view. The remaining seven directions in the fixed ring order (front, front_right, right, back_right, back, back_left, left, front_left) are then derived automatically from that anchor rather than edited one by one.
4. Invalid-view cleanup
   Mark strongly occluded or unusable RGB views as invalid. These views should not survive as reviewed evidence just because they were captured geometrically.
5. Class-name synchronization
   If the weak class hint is not suitable, edit class_name before auto-labeling so the prompt receives a cleaner prior.
6. Auto-label execution
   Run auto-label by current landmark, by class, or globally. The web exposes all three actions: Single, By Class, and All.
7. Auto-label review
   Inspect auto_label_category, auto_label_subcategory, auto_label_description, and confidence. If the proposal is good, use Approve Auto Label; otherwise edit the final semantic fields manually.
8. Manual correction and save
   Final reviewed fields are landmark_category, landmark_subcategory, and landmark_description. Save manual fixes if the automatic suggestion is incomplete or wrong.

Important page areas:

• Left landmark list
  Shows class grouping, review status, and auto-label status so reviewers can move through one semantic group at a time.
• Point-cloud evidence area
  Use this area to confirm that the candidate remains geometrically coherent and is not a fragmented or unstable instance.
• Eight-view RGB area
  Use this area to confirm the representative image, one direction anchor, and invalid-view removal.
• Auto-label controls
  Use these controls to start, monitor, cancel, or clear annotation jobs.
• Final semantic fields
  Use these fields to publish the reviewed landmark semantics that later prompt templates depend on.

Artifacts written during this workflow:

• landmarks_review/<scene>.valid_instances.json
• landmarks_review/<scene>.review_log.jsonl
• landmarks_review/auto_label_debug/ (when debug export is enabled)

Stage 2 internal review workspace. Point-cloud evidence, multiview RGB evidence, review-state controls, and auto-label approval are combined here so that Stage 2 Step 2-4 can be completed in one continuous workflow.

Launch:

python scripts/uav_dualcog/stage3_generate_traj.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode web

The Stage 3 workbench exposes a multi-page mission and task interface. The main pages are:

• Behavior Library
• Missions
• Review
• Generate
• Dataset
• Experiments
• Results
• Metrics

Before treating a page as empty, first switch the top-right scene, task, mission, or manifest selector. Several Stage 3 pages only populate after an active selection is made.

Recommended use of each page:

Behavior Library

• Inspect composite classes, atomic classes, parameter ranges, and defaults.
• Use this page to understand which atomic maneuvers a composite template expands into before trajectory generation.

Missions

• Select target landmarks and configure mission mode, generation count, template selection rule, and optional atomic overrides.
• Generate panorama/preview media first, then regenerate preview or final task video when needed.
• Use this page for interactive mission prototyping and for checking whether a landmark is well matched to the intended composite or atomic behavior.

Review

• Browse generated candidate missions and mark them as approved, pending, or rejected.
• Use this page to remove visually poor or semantically ambiguous missions before they are turned into task rows.

Generate

• Convert approved candidates into Stage 3 benchmark manifests.
• Control self-state and environmental task forms, sample count, seed, and whether temporal localization is included.
• Use this page for spot checks and small controlled reruns. For released-scale Stage 3 generation, we recommend task_pipeline.py --stage stage3 --phase data/render.

Dataset

• Load a manifest and preview the generated sample rows.
• Use this page to inspect prompt-facing fields, answer targets, intervals, and auxiliary media such as overview images and keyframe boards.

Experiments

• Choose the active manifest, input one or more model aliases, and launch run jobs.
• Control upload resolution, JPEG quality, concurrency, RPM/TPM, and optional prompt toggles such as whether flight description or keyframe evaluation is enabled.
• Use this page for targeted reruns and qualitative debugging. For released-scale experiment sweeps, we recommend task_pipeline.py --stage stage3 --phase experiment.

Results

• Inspect each run report and browse sample-level outputs.
• Use this page to check whether errors are caused by parsing, semantics, or interval prediction.

Metrics

• Compare models by summary cards, grouped bars, full metric tables, and progress tables.
• Export CSV if you want to aggregate Stage 3 metrics offline.

Operational recommendation:

• Use the Stage 3 web to inspect, debug, and spot-check.
• Use task_pipeline.py --stage stage3 --phase selection/data/render/experiment for the full released workflow so that runs remain batchable and reproducible.

Stage 3 behavior library. This page presents the hierarchical relation between composite inspection classes and atomic motion primitives before trajectory generation begins.

Stage 3 mission generation. Reviewers select landmarks, mission families, and generation options here, then render panorama, preview, or final task videos for interactive spot checks.

Stage 3 manifest generation. Approved candidates are converted into benchmark-facing task rows here; the generated manifest can then be inspected in the dataset browser together with sample media and interval labels.

Stage 3 dataset browser. This page is used to verify reference images, overview boards, sample videos, answer targets, and interval annotations before experiments are launched.

Stage 3 results page. Run-level summaries and sample-level predictions are browsed here to distinguish semantic mistakes, parsing failures, and temporal-localization errors.

Stage 3 metrics page. Summary cards, grouped comparisons, full tables, and progress views support quick diagnosis before exporting CSV for offline aggregation.

Launch:

python scripts/uav_dualcog/stage4_qa_generate_and_eval.py \
  --config configs/uav_dualcog/task_airsim_env_7.yaml \
  --scene-id env_7 \
  --mode web \
  --port 20264

The Stage 4 workbench is organized around five pages:

• Generate
• Dataset
• Experiments
• Results
• Metrics

Before treating a page as empty, first switch the top-right scene, task type, manifest, or report selector. Several Stage 4 pages only render detailed content after an active selection is chosen.

Recommended use of each page:

Generate

• Configure task strategy, reference-view definition, task types, category filters, difficulty, and sample counts.
• Use the estimator to check how many rows the current choice is likely to produce before actually writing a manifest.
• Use this page for interactive validation and spot checks. For released-scale Stage 4 generation, we recommend task_pipeline.py --stage stage4 --phase data/render.

Dataset

• Browse manifest summaries and sample previews.
• Use this page to verify that reference images, query images, answer options, and bbox targets are aligned correctly.

Experiments

• Select a manifest, choose one or more models, set upload size/quality, concurrency, and limits, then launch jobs.
• Use this page for small to medium comparative runs and prompt/debug checks.
• For released-scale benchmark runs and comparative sweeps, we recommend task_pipeline.py --stage stage4 --phase experiment.

Results

• Open a report and inspect per-sample outputs.
• Use this page to confirm whether an error comes from option selection, bbox grounding, or parser failure.

Metrics

• View summary cards, grouped comparisons, the experiment matrix, and progress tables.
• Export CSV for downstream analysis when needed.

Operational recommendation:

• Use the Stage 4 web to validate manifest quality and inspect experiment outputs qualitatively.
• Use task_pipeline.py --stage stage4 --phase selection/data/render/experiment when you need released-scale batch generation or large model sweeps.

Stage 4 task generation. Sampling strategy, task types, difficulty settings, and per-landmark limits are configured here before new image-QA manifests are written.

Stage 4 dataset browser. This page is the fastest place to verify that reference images, query images, option ordering, and bbox targets remain visually aligned.

Stage 4 experiments page. Model aliases, upload settings, concurrency, and rate limits are managed here for qualitative reruns and small-to-medium comparison jobs.

Stage 4 results page. Per-run summaries and sample-level outputs make it easy to inspect whether a failure comes from option selection, bbox grounding, or parser behavior.

Stage 4 metrics page. Summary cards, grouped comparisons, experiment matrices, and progress tables provide a compact view of image-task evaluation quality.

Purpose: run model evaluation on released task manifests without redoing scene construction.

# Stage 3 experiments
python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage3 --phase experiment \
  --experiment-models openai/gpt-5.3-chat Qwen/Qwen3.5-9B-Instant

# Stage 4 experiments
python scripts/uav_dualcog/task_pipeline.py \
  --spec configs/uav_dualcog/task_pipeline/task_pipeline_uav_dualcog_v1.yaml \
  --stage stage4 --phase experiment \
  --experiment-models openai/gpt-5.3-chat Qwen/Qwen3.5-4B-Thinking

If you only want to verify interface wiring (without real model calls), use --help on stage/pipeline scripts and validate config parsing paths first.

python scripts/uav_dualcog/stage1_collect_pcd.py --help
python scripts/uav_dualcog/stage2_landmark_label.py --help
python scripts/uav_dualcog/probe_airsim_mapbound.py --help
python scripts/uav_dualcog/stage3_generate_traj.py --help
python scripts/uav_dualcog/stage4_qa_generate_and_eval.py --help
python scripts/uav_dualcog/task_pipeline.py --help
python scripts/uav_dualcog/mock_api_runtime_check.py --config configs/uav_dualcog/common_api_runtime.yaml

These checks confirm runnable CLI interfaces before launching long construction or experiment jobs.

If you use FineCog-Nav in your research, please cite the following paper:

@misc{liu2026uavdualcog,
      title={Knowing the Self, Understanding the World: A Dual-Cognition Benchmark for UAV Spatio-temporal Reasoning with MLLMs}, 
      author={Like Liu and Zhengzheng Xu and Haitao He and Hongzhe Li and Shuchang Zhang and Dian Shao},
      year={2026},
      eprint={2607.16193},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.16193}, 
}

Some components modified from AerialVLN and OpenFly. Thanks sincerely.