| --- |
| license: mit |
| pretty_name: NEXUS FPV Physics Dataset (forest, dataset / auto) |
| tags: |
| - robotics |
| - reinforcement-learning |
| - drone |
| - fpv |
| - physics-simulation |
| - synthetic |
| - trajectory |
| task_categories: |
| - reinforcement-learning |
| - robotics |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: telemetry.csv |
| --- |
| |
| # NEXUS FPV Physics Dataset Sampler by webXOS |
|
|
| Auto-generated small FPV drone flight-telemetry and reinforcement-learning for experience sample dataset captured directly in the browser |
| by **NEXUS FPV**. Each row is one physics tick recorded while a drone flew through a waypoint course, either under manual control or the built-in PID auto-pilot. |
|
|
| Play the game and make your own datasets: https://webxos.itch.io/nexus-fpv or download it from the /gym/ folder of this repo. |
|
|
| - **Generator:** NEXUS FPV Physics Dataset Generator |
| - **Export timestamp:** 2026-07-17T08:34:04.112Z |
| - **Scene:** forest |
| - **Game mode:** dataset |
| - **Control source:** auto |
| - **Total telemetry records:** 170 |
| - **Total RL experiences:** 179 |
| - **Recording duration:** 12.3s |
| - **Epochs / laps completed:** 0 |
| - **Collisions:** 0 |
| - **Path length:** 39m |
|
|
| ## Files |
|
|
| | File | Description | |
| |---|---| |
| | `telemetry.csv` | Flat, tabular telemetry — one row per physics tick (default HF viewer config). | |
| | `telemetry.jsonl` | Same telemetry data, newline-delimited JSON. | |
| | `experiences.jsonl` | `(state, action, reward, next_state, done, q_values)` tuples sampled during auto-pilot flight, ready for offline RL / imitation learning. | |
| | `metadata.json` | Full run metadata, including neural-network hyperparameters and waypoint pattern. | |
|
|
| ## `telemetry.csv` / `telemetry.jsonl` columns |
|
|
| | Column | |
| |---| |
| | `timestamp` | |
| | `mode` | |
| | `scene` | |
| | `epoch` | |
| | `iteration` | |
| | `pos_x` | |
| | `pos_y` | |
| | `pos_z` | |
| | `vel_x` | |
| | `vel_y` | |
| | `vel_z` | |
| | `rot_pitch` | |
| | `rot_yaw` | |
| | `rot_roll` | |
| | `target_x` | |
| | `target_y` | |
| | `target_z` | |
| | `current_waypoint` | |
| | `waypoint_name` | |
| | `distance_to_target` | |
| | `pattern_progress` | |
| | `q_value` | |
| | `reward` | |
| | `cumulative_reward` | |
| | `loss` | |
| | `epsilon` | |
| | `collisions` | |
| | `path_length` | |
| | `camera_mode` | |
| | `boost` | |
| | `auto_mode` | |
|
|
| ## Neural network configuration |
|
|
| - Architecture: `[8,24,9]` |
| - Learning rate: 0.001 |
| - Gamma: 0.95 |
| - Epsilon (final): 0.9323284674748037 (min 0.05, decay 0.998) |
| - Action space size: 9 |
| - State vector size: 8 |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("path/to/this/dataset") |
| print(ds["train"][0]) |
| ``` |
|
|
| ## Intended use |
|
|
| Trajectory sample data is for training/evaluating drone flight-control policies, imitation learning, offline RL, and |
| physics-based motion prediction. Data is synthetic, generated entirely from an in-browser physics simulation — |
| no real-world flight or personal data is included. |
|
|
|
|
| ## License |
|
|
| MIT |
