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motoko-spatial-1-1b
monodox/motoko-spatial-1-1b
motoko-spatial-1-1b is a robotics model from monodox. Use it for the robotics task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
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 re…
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At a glance
- Task
- Robotics
- License
- apache-2.0
- Model type
- motoko_spatial
- Access
- Public
- Created
- Apr 22, 2026
- Updated
- Apr 22, 2026
- SHA
- 1efb4906
Model card
From the Hugging Face model README
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.
- Task
- Robotics
- Type
- motoko_spatial
- License
- apache-2.0
- Created
- Apr 22, 2026
- Updated
- Apr 22, 2026