---
license: mit
language:
- en
task_categories:
- translation
- text-generation
- robotics
tags:
- robotics
- tool-calling
- rc-car
- langchain
- nodemcu
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: data/rc_dataset.csv
---

# RC-Car Command Dataset

A hand-authored dataset for training a tiny language model that maps a **natural-language command** to a **structured list of tool calls** (LangChain/LangGraph style), to drive an RC car via a NodeMCU.

- **File:** `data/rc_dataset.csv`
- **Columns:** `input` (natural language), `output` (JSON tool-call list)
- **Rows:** 700
- **Task type:** sequence-to-sequence (translation)

## Output grammar

Each `output` is a JSON array of tool-call objects:

```json
[{"name":"Forward","args":{"duration":2}},{"name":"Turn_Left","args":{}},{"name":"Stop","args":{}}]
```

### Tools

| Tool | args | Meaning |
|------|------|---------|
| `Forward` | `{"duration": n}` | drive forward n seconds (n = 1–10) |
| `Backward` | `{"duration": n}` | drive backward n seconds (n = 1–10) |
| `Turn_Right` | `{}` | turn right 90° |
| `Turn_Left` | `{}` | turn left 90° |
| `Stop` | `{}` | stop the motors |

### Labeling rules (applied to every row)

1. **Default duration = 2.** If a Forward/Backward command has no explicit time, duration is `2`.
2. **Word-numbers → digits** ("five seconds" → `5`). Vague durations ("a bit", "briefly", "a moment") → default `2`.
3. **Durations are clamped to 1–10.**
4. **Turns take no arguments.**
5. **Exactly one trailing `Stop`** is always present — appended even when the user didn't say "stop", and never duplicated when they did.

## Composite shape templates (stylized, 90° turns only)

Notation: `F(n)`=Forward, `B(n)`=Backward, `R`=Turn_Right, `L`=Turn_Left, `S`=Stop.

| Shape | Decomposition |
|-------|---------------|
| U-turn | `F(2) R R F(2) S` |
| Square | `F(2) R F(2) R F(2) R F(2) R S` |
| Triangle | `F(2) R F(2) R F(2) R S` (stylized) |
| Circle | `F(1) R F(1) R F(1) R F(1) R S` (short segments) |
| Star | `F(2) R R F(2) R R F(2) R R F(2) R R F(2) R R S` |
| Zigzag | `F(2) R F(2) L F(2) R S` |
| Figure-8 | `F(1) R F(1) R F(1) R F(1) R F(1) L F(1) L F(1) L F(1) L S` |

> Because turns are fixed at 90°, only Square is geometrically exact. Triangle, Circle, Star, and Figure-8 are **stylized approximations** built from 90° steps. They are easy to retune — just change the template above and regenerate those rows.

### Left-handed (counterclockwise) shape variants

The first 500 rows draw all shapes clockwise (Turn_Right). Rows 501–700 add **left-handed variants** for balance — same templates with `Turn_Right`↔`Turn_Left` swapped, triggered by phrasings like "to the left", "going left", "counterclockwise". The Zigzag/Figure-8 variants are the mirror image (start with the opposite turn). This is what brings Turn_Left coverage close to parity with Turn_Right.

## Coverage (700 rows)

The original 500 rows (see table below) plus 200 rebalancing rows added to fix tool/duration imbalance:

| Category | Rows |
|----------|------|
| Forward (durations, defaults, word-numbers, synonyms) | 60 |
| Backward | 50 |
| Turn left only | 30 |
| Turn right only | 30 |
| Stop only | 20 |
| 2-action compounds | 90 |
| 3–4 action compounds | 80 |
| U-turn | 25 |
| Square | 25 |
| Circle | 20 |
| Triangle | 20 |
| Star | 15 |
| Zigzag | 20 |
| Figure-8 | 15 |
| **— rebalancing rows (501–700) —** | |
| Backward primitives + compounds (all durations 1–10) | ~90 |
| Forward with long durations (3–10) | ~40 |
| Turn-left-heavy compounds | ~40 |
| Left-handed shape variants (square/circle/triangle/u-turn/star/zigzag/figure-8) | ~30 |

### Resulting balance (tool occurrences across all rows)

| Tool | Occurrences |
|------|------------:|
| Forward | 1029 |
| Turn_Right | 707 |
| Stop | 700 |
| Turn_Left | 496 |
| Backward | 211 |

Duration spread: `2` is still the most common (it's the default), `1` is common (circles/figure-8 use 1s segments), and `3`–`10` each appear 18–44 times.

## Notes for training (next step)

- Treat this as seq2seq. Build two small vocabularies (input words; output JSON tokens) plus special tokens `<pad>`, `<bos>`, `<eos>`, `<unk>`.
- The JSON output is verbose and regular — tokenize it structurally (tool names, `"name"`, `"args"`, `"duration"`, digits, and each `{ } [ ] : ,` as tokens) so the vocabulary stays tiny.
- Hold out a small portion (e.g. 10%) as a test set with phrasings not seen in training to measure real generalization.
- The longest outputs are the Star (16 calls) and Figure-8 (17 calls) — use these to choose your max sequence length for padding.