---
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