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license: apache-2.0
tags:
- robotics
- haptics
- spatial-understanding
- touch-sensing
- force-estimation
pipeline_tag: robotics
---
# Motoko Spatial 1B
Motoko Spatial 1B is a foundation model for 3D haptic spatial understanding in robotics. It takes raw sensor array input from distributed touch sensors across a robot surface and outputs spatial force maps, contact region predictions, and pressure distribution fields.
## Model Details
- **Model type:** 3D haptic spatial foundation model
- **Parameters:** 1B
- **Architecture:** Hybrid CNN + Transformer
- **Input:** 3D coordinate arrays and sensor pressure grids
- **Output:** Force field maps, contact region masks, and pressure heatmaps
- **License:** Apache-2.0
## Intended Use
Motoko Spatial 1B is designed for robotics systems that need dense touch and contact understanding from distributed tactile sensors.
Primary use cases include:
- Dexterous multi-finger manipulation
- Full-body robot touch sensing
- Terrain and surface contact mapping
- Collision detection
- Safe human-robot contact
## Inputs
The model expects structured haptic sensor input containing:
- 3D sensor coordinates
- Pressure grid values
- Optional force and torque channels
- Sensor timing or sampling metadata when available
Raw haptic arrays should be converted into model input tensors with `preprocessor/feature_extractor.py`.
## Outputs
The model produces spatial predictions for downstream robotics control and perception:
- Spatial force field maps
- Contact region masks
- Pressure distribution heatmaps
## Repository Files
| File | Description |
| --- | --- |
| `config.json` | Architecture definition, including layers, attention heads, hidden size, channel count, and spatial dimensions. |
| `configs/sensor_config.yaml` | Sensor array layout, sampling rate, axes, channel names, and physical units. |
| `preprocessor/preprocessor_config.json` | Signal normalization, channel statistics, windowing, and resampling configuration. |
| `model/model.safetensors` | Actual trained model weights. The current scaffold contains a placeholder until trained weights are added. |
| `model/model.safetensors.index.json` | Weight index used for loading sharded or indexed safetensors weights. |
| `preprocessor/feature_extractor.py` | Converts raw haptic arrays into normalized model input tensors. |
| `tokenizer_config.json` | Signal tokenizer metadata for quantized or discretized haptic tokens. |
| `tokenizer.json` | Minimal tokenizer vocabulary placeholder. |
| `configs/training_config.yaml` | Training hyperparameters and checkpoint cadence. |
| `examples/inference.py` | Basic inference preprocessing example. |
| `examples/spatial_map.py` | Spatial force map construction example. |
## Limitations
This repository is currently a minimal Hugging Face model scaffold. The included `model/model.safetensors` file is a placeholder and should be replaced with trained weights before production use.
## Citation
Citation information will be added when a technical report or paper is available.
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