monodox/motoko-1-1b

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

.
├── 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

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

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