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.