monodox/motoko-embedding-1-1b

Motoko Embedding 1B

Motoko Embedding 1B is a foundation embedding model for haptic signal representation in robotics. It encodes raw force, torque, pressure, and vibration signals into rich fixed-dimension vector embeddings for retrieval, search, and cross-modal fusion.

Model Summary

• —Model type: Encoder-only Transformer
• —Parameters: 1B
• —Input: Force, torque, pressure, vibration sequences
• —Output: Fixed-dimension embedding vectors
• —License: Apache 2.0

Intended Uses

• —Semantic search over haptic datasets
• —Cross-modal alignment with vision and language
• —Haptic RAG pipelines for robotic agents
• —Dataset indexing and similarity clustering
• —Downstream fine-tuning with LoRA adapters

Architecture

Motoko Embedding 1B uses a signal-aware preprocessing stack followed by an encoder-only Transformer. Multichannel sensor streams are windowed, normalized, projected into token embeddings, and aggregated into a single fixed-size embedding representation.

Key design points:

• —Temporal patching over multiaxis haptic sequences
• —Rotary position embeddings for long-context signal modeling
• —Mean pooling over the final hidden states for embedding extraction
• —Optional projection head for cross-modal alignment

Input Format

The model expects synchronized haptic sequences containing one or more of the following modalities:

• —Force
• —Torque
• —Pressure
• —Vibration

Default sensor assumptions are defined in `configs/sensor_config.yaml`. Signal normalization and windowing parameters are defined in `preprocessor/preprocessor_config.json`.

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
│   ├── embedding_search.py
│   └── cross_modal.py
└── .gitattributes

Key Files

Usage

Load the processor

from preprocessor.feature_extractor import HapticFeatureExtractor

extractor = HapticFeatureExtractor.from_pretrained(".")

Basic embedding inference

import numpy as np

from preprocessor.feature_extractor import HapticFeatureExtractor

extractor = HapticFeatureExtractor.from_pretrained(".")
sample = np.random.randn(1024, 12).astype("float32")
features = extractor(sample)

print(features["input_values"].shape)
print(features["attention_mask"].shape)

See `examples/inference.py` for a complete example.

Training

Baseline training parameters are provided in `configs/training_config.yaml`. These values are intended as a starting point for pretraining or continued domain adaptation, not as a claim of the exact recipe used for a released checkpoint.

Limitations

• —Performance depends heavily on sensor calibration and synchronization quality.
• —Out-of-distribution hardware setups may require updated preprocessing statistics.
• —Cross-modal alignment quality depends on the paired supervision used during training.
• —This repository scaffold does not include production weights.

Citation

@misc{motoko_embedding_1b,
  title        = {Motoko Embedding 1B},
  author       = {Motoko},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/}}
}