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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… See the full description on the dataset page: https://huggingface.co/datasets/bfz111/STDP-Dataset.

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Dataset Card

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

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

text

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

text

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

Install the Hugging Face Hub client:

bash

pip install -U huggingface_hub

Read the desired JSONL manifest, then use its world and trajectory_name fields to download the corresponding archive:

python

import json
from huggingface_hub import hf_hub_download

repo_id = "bfz111/STDP-Dataset"

manifest = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename="splits/test_same.jsonl",
)

with open(manifest, "r", encoding="utf-8") as stream:
    sample = json.loads(next(stream))

archive = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=(
        f"archives/{sample['world']}/"
        f"{sample['trajectory_name']}.tar"
    ),
)
print(archive)

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

bash

tar -xf /path/to/trajectory_0127_01.tar -C /path/to/local_dataset

After extraction, trajectory_path and the four entries in images resolve directly against /path/to/local_dataset.

To download the complete release, use snapshot_download:

python

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="bfz111/STDP-Dataset",
    repo_type="dataset",
    local_dir="STDP-Dataset",
)

License

The dataset is released under the MIT License. See LICENSE for details.

Citation

Coming soon.