---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- haptics
- time-series
- robotics
- sensor-fusion
- mamba
- transformer
pipeline_tag: time-series-classification
---

# Motoko 1B

Motoko 1B is the core foundation model of the Motoko family: a general-purpose haptic model pretrained across touch, force, and sensor interaction data.

## Model Details

- **Parameters:** 1B
- **Architecture:** Mamba / Hybrid CNN + Transformer
- **Input:** Force, torque, pressure, vibration time-series
- **Output:** Next-state prediction and signal classification
- **Sequence Length:** Up to 2048 timesteps
- **Sampling Rate:** Up to 1 kHz
- **License:** Apache 2.0

## Intended Use

Motoko 1B is designed for:

- Haptic signal classification and understanding
- Grasp stability prediction
- Material and texture recognition from touch
- Force state forecasting
- Fine-tuning as a base for downstream haptic tasks
- Serving as the parent model for Motoko LoRA adapters

## Repository Layout

```text
.
├── README.md
├── config.json
├── tokenizer_config.json
├── tokenizer.json
├── model/
│   ├── model.safetensors
│   └── model.safetensors.index.json
├── preprocessor/
│   ├── preprocessor_config.json
│   └── feature_extractor.py
├── configs/
│   ├── training_config.yaml
│   └── sensor_config.yaml
├── examples/
│   ├── inference.py
│   ├── grasp_stability.py
│   ├── material_recognition.py
│   └── force_forecasting.py
└── .gitattributes
```

## Input Format

The model expects multichannel haptic time-series windows containing one or more of the following modalities:

- Force
- Torque
- Pressure
- Vibration

Signals should be normalized and resampled according to `preprocessor/preprocessor_config.json` before inference.

## Tasks

### Grasp Stability Prediction

Given a short force or tactile sequence collected during grasping, the model predicts whether a grasp is stable or likely to fail.

### Material Recognition

Given touch-only or force-plus-vibration sequences, the model classifies the material category or texture family.

### Force Forecasting

Given a recent trajectory of haptic observations, the model predicts the next force state or short horizon continuation.

## Example Usage

```python
from pathlib import Path

import numpy as np

from preprocessor.feature_extractor import MotokoFeatureExtractor

extractor = MotokoFeatureExtractor.from_config(
    Path("preprocessor/preprocessor_config.json")
)

sample = {
    "force": np.random.randn(256, 3),
    "torque": np.random.randn(256, 3),
    "pressure": np.random.randn(256, 16),
}

features = extractor(sample)
print(features["input_values"].shape)
```

## Training

Base training hyperparameters are stored in `configs/training_config.yaml`, and sensor assumptions are defined in `configs/sensor_config.yaml`.

## Limitations

- This repository currently contains scaffold configuration and examples.
- `model/model.safetensors` is a placeholder and should be replaced with actual trained weights.
- Final tokenizer and preprocessing values should be aligned with the released checkpoint.

## Citation

```bibtex
@misc{motoko1b,
  title        = {Motoko 1B},
  author       = {Motoko Team},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/}},
  note         = {Foundation model for haptic understanding and forecasting}
}
```