bfz111/STDP-Dataset

STDP Dataset

This repository provides the data used to train, validate, and evaluate Signal Temporal Logic-Guided Diffusion Policy (STDP) for UAV Visual Navigation without Global Semantic Maps.

Dataset Contents

Each trajectory sample combines a temporal-logic navigation instruction, multi-view visual observations, and a reference motion trajectory.

Component	Contents	Intended use
Semantic STL specification	Object-level temporal and logical constraints	Defines the navigation task presented to the policy
Coordinate-grounded STL specification	The same specification with object references mapped to XYZ coordinates	Supports supervision, analysis, and geometry-aware evaluation
Multi-view RGB observations	Synchronized front, down, left, and right camera images	Provides local visual context without a task-level global semantic map
Reference trajectory	CSV records containing absolute XYZ positions, camera yaw, frame indices, and image filenames	Supplies waypoint supervision and synchronizes motion with observations
Split manifests	JSONL records linking instructions, scenes, trajectories, and image directories	Defines the released training, validation, and evaluation subsets
Trajectory archives	One tar archive per trajectory	Enables selective downloading while preserving the original directory layout
Simulation environment	Four Gazebo Classic worlds, drone and base models, plugin source, materials, and capture scripts	Supports rebuilding the scenes and replaying trajectories

Scenes and Task Types

The data covers four furnished indoor scenes. world_v1 is the primary scene used by the training, validation, and same-scene evaluation splits. world_v2, world_v3, and world_v4 provide different indoor layouts for cross-layout evaluation.

The released trajectories cover four task families:

Task type	Objective
Reach-Avoid	Reach a specified target while avoiding constrained regions
Multi-Target	Visit multiple targets under temporal and logical constraints
Either-Or	Satisfy one of multiple valid target alternatives
Door Puzzle	Obey ordered reachability and avoidance requirements

Data Splits

Manifest	Purpose
splits/train.jsonl	Stage 1 policy training in the primary scene
splits/validation.jsonl	Model selection and validation in the primary scene
splits/test_same.jsonl	Evaluation on held-out trajectories from the primary scene
splits/test_cross.jsonl	Evaluation in the three cross-layout scenes

Each line in a split manifest is a JSON object with the following main fields:

Field	Description
task_type	Navigation task family
stl	Semantic STL specification
world	Scene identifier
trajectory_name	Unique trajectory name within a scene
trajectory_path	Repository-relative path to the reference CSV after extraction
images	Repository-relative paths to the four camera directories after extraction

Repository Layout

README.md
LICENSE
simulation_environment.tar.gz
simulation_environment.tar.gz.sha256
simulation/
  README.md
  world/home_room_v{1,2,3,4}.world
  models/{simple_drone,sun,ground_plane}/
  plugins/{SimpleDronePlugin.cc,CMakeLists.txt}
  media/materials/{scripts,textures}/
  build.sh
  run.sh
  gazebo_env.sh
  datasets_construct.sh
  smoke.sh
  examples/smoke.csv
  ENVIRONMENT.txt
  VALIDATION.md
  SOURCE_FILES.json
  SHA256SUMS
  validation/
splits/
  train.jsonl
  validation.jsonl
  test_same.jsonl
  test_cross.jsonl
archives/
  world_v1/<trajectory_name>.tar
  world_v2/<trajectory_name>.tar
  world_v3/<trajectory_name>.tar
  world_v4/<trajectory_name>.tar

Each trajectory archive contains the CSV and all synchronized images using the original repository-relative paths. For example:

data/world_v1/trajectory/trajectory_0127_01.csv
data/world_v1/images/trajectory_0127_01/images_front/
data/world_v1/images/trajectory_0127_01/images_down/
data/world_v1/images/trajectory_0127_01/images_left/
data/world_v1/images/trajectory_0127_01/images_right/

Usage

Download files through the anonymous dataset endpoint. Install the HTTP client:

pip install requests

The following example downloads one split and its first trajectory archive:

import json
from pathlib import Path
import requests

BASE_URL = "https://anonymous-hf.com/api/a/h8or94tutz4u/resolve/"
DATASET_ROOT = Path("STDP-Dataset")


def download(relative_path):
    destination = DATASET_ROOT / relative_path
    destination.parent.mkdir(parents=True, exist_ok=True)
    partial = destination.with_name(destination.name + ".partial")
    with requests.get(BASE_URL + relative_path, params={"release": "anonymous-20260929-complete"}, stream=True, timeout=120) as response:
        response.raise_for_status()
        with partial.open("wb") as output:
            for chunk in response.iter_content(chunk_size=1024 * 1024):
                output.write(chunk)
    partial.replace(destination)
    return destination


manifest = download("splits/test_same.jsonl")
with manifest.open(encoding="utf-8") as stream:
    sample = json.loads(next(stream))
archive = download(
    f"archives/{sample['world']}/{sample['trajectory_name']}.tar"
)
print(archive)

Extract the archive from the root of the local dataset directory:

tar -xf STDP-Dataset/archives/world_v1/trajectory_0587_05.tar -C STDP-Dataset

After extraction, trajectory_path and the four entries in images resolve relative to STDP-Dataset/.

For all released trajectories, use the helper above to download the four split manifests and their unique archives. This downloads approximately 145 GiB:

archives = set()
for split in ("train", "validation", "test_same", "test_cross"):
    manifest = download(f"splits/{split}.jsonl")
    with manifest.open(encoding="utf-8") as stream:
        for line in stream:
            sample = json.loads(line)
            archives.add(
                f"archives/{sample['world']}/{sample['trajectory_name']}.tar"
            )
for relative_path in sorted(archives):
    download(relative_path)

Simulation Environment

The simulation release includes all four indoor worlds, the drone's four-view camera model, SimpleDronePlugin source and CMake configuration, base models, custom materials and their textures, and build/playback/capture scripts.

• Download the environment archive
• Download its SHA256 checksum
• Browse the dataset files (open simulation/)
• Read the detailed setup guide
• Read the validation record

Download and Build

Download just the environment without the trajectory archives:

Using the download helper from the Usage section:

for filename in (
    "simulation_environment.tar.gz",
    "simulation_environment.tar.gz.sha256",
):
    download(filename)

The reference platform is Ubuntu 20.04.6 LTS and Gazebo Classic 11.15.1. The plugin uses the Gazebo C++ API directly and does not require ROS or PX4. It is not compatible with the newer Gazebo simulator's gz sim command.

On Ubuntu 20.04 with a Gazebo 11 package source configured:

sudo apt-get update
sudo apt-get install gazebo11 libgazebo11-dev build-essential cmake pkg-config \
  python3 python3-numpy python3-pil xvfb x11-utils mesa-utils pciutils

cd STDP-Dataset
sha256sum -c simulation_environment.tar.gz.sha256
tar -xzf simulation_environment.tar.gz
cd simulation_environment
sha256sum -c SHA256SUMS
chmod +x *.sh
bash build.sh

Run the build in a system Bash shell, preferably after leaving Conda. The plugin is compiled locally to plugins/build/libSimpleDronePlugin.so; machine-specific binaries and CMake caches are excluded from the release. If gazebo11 has no installation candidate, configure an appropriate Gazebo Classic package source first. A full repository download also includes the same unpacked files under simulation/; you can build from that directory instead.

Check Rendering and Replay Released Trajectories

From the environment directory, run a one-waypoint capture check:

SIMPLE_DRONE_RENDER_BACKEND=xvfb bash smoke.sh v1

Use v2, v3, or v4 to select another scene. Success prints PASS and creates four 640 x 480 images plus capture metadata in datasets/smoke_world_vN/. Even gzserver requires an OpenGL/X rendering context for camera sensors. Software rendering can be slow; adjust SIMPLE_DRONE_RUN_TIMEOUT if needed.

To replay a trajectory downloaded and extracted using the instructions above, copy its CSV into the matching scene directory. For example, from the environment directory, with the dataset root one level above the environment directory:

mkdir -p datasets/world_v1/trajectory
cp ../data/world_v1/trajectory/trajectory_0127_01.csv \
  datasets/world_v1/trajectory/
SIMPLE_DRONE_QUIT_ON_FINISH=1 ./run.sh v1 trajectory_0127_01.csv

Use a working copy: capture updates the input CSV in place with frame indices, captured yaw, and image filenames. Images are written under datasets/world_v1/images/trajectory_0127_01/images_{front,down,left,right}/. For batch capture, run ./datasets_construct.sh datasets/world_v1.

Validation Scope

All four worlds passed resource and structural checks, and the plugin compiled successfully from source on the reference system. The v1 smoke check passed image quality validation and produced all four views. Under Xvfb, the plugin used its existing single-camera sweep fallback after rejecting physical camera frames; this is not a claim that all four physical cameras captured successfully without fallback. Runtime capture in v2/v3/v4, installation on a separate clean machine, and full benchmark reproduction were not validated for this release.

This package reproduces the simulation assets and playback/capture setup. Training, trajectory planning, policy inference, and paper metrics additionally require the main project code and data. Exact system versions, file provenance, checksums, and build/capture logs are included with the environment.

License

The dataset is released under the MIT License. See LICENSE for details. Bundled third-party Gazebo base models and textures remain subject to their upstream licenses and are not relicensed by the dataset license. Their sources are recorded in simulation/SOURCE_FILES.json.

Citation

Coming soon.