Stellitron
Applying latent reasoning tokens to LMMs for implicit inference of spatial relationships and object affordances in unstructured environments.
Applying latent reasoning tokens to LMMs for implicit inference of spatial relationships and object affordances in unstructured environments.
The Fragility of Robotic Perception
Robotics adoption is stalled by brittle vision pipelines that fail in unstructured environments without manual labeling or expensive depth sensors.
- ⚠High failure rates in non-uniform or novel environments
- ⚠Prohibitive costs of manual annotation and dataset curation
- ⚠Rigid systems unable to infer physical constraints like 'obstructing' or 'sturdy'
Measured Impact
The Stellitron LMM Layer
A foundational model layer using latent reasoning tokens to infer object affordances directly from standard RGB input.
Token Injection
Injecting proprietary reasoning tokens into the LMM latent space.
Implicit Inference
Predicting physical properties like 'liftable' or 'obstructing' without explicit depth data.
System Architecture
- Standard RGB Video/Images
- Natural Language Task Description
- Latent Reasoning Token Injector
- Multimodal Transformer Backbone
- Affordance Mapping Head
- Spatial Relationship Graph
- Affordance Probability Map
- Action Trajectory Proposals
- ROS2
- Nvidia Isaac Gym
- PyBullet
Why This Is Hard to Copy
- ✓Proprietary alignment of tokens with real-world physics outcomes
- ✓Unique embodied interaction dataset
- Algorithmic IP in token injection methodology
- Zero-shot generalization capabilities
- Sits above the hardware layer, compatible with any arm or mobile base
- Dataset compounding through cross-platform interaction data
- High switching costs once integrated into OEM control loops
- Hardware-agnostic scale allows faster data flywheel than hardware-locked competitors
Market Opportunity
“The convergence of LMMs and robotics will unlock $2T in global productivity gains by 2030.”
Competitive Landscape
Competitive Landscape
| Feature | Covariant | Figure AI | Stellitron |
|---|---|---|---|
| Zero-Shot Generalization | Low | Medium | High |
| Hardware Agnosticism | Medium | Low | High |
| RGB-Only Affordance | Low | Low | High |
Business Model
Enterprise API
Annual platform license for industrial integrators.
Inference Credits
Usage-based billing for real-time scene understanding tasks.
Deployment Services
One-time setup and model fine-tuning for specific facility geometries.
Traction & Validation
“Stellitron's implicit affordance mapping allowed our robots to handle novel packaging types in days, not months.”
Financial Projections
Yearly Revenue Projections
Operating Assumptions & Burn Logic
Key Performance Indicators
The Ask
Exit Strategy
Exit Scenarios
Comparable Exits
Risk Analysis
Risk Analysis & Mitigation
Latency in real-time edge processing
Model quantization and NPU-specific optimization partnerships.
Incumbent competition from Big Tech
Focus on niche physical affordance data that general models lack.
Safety standards for autonomous systems
Early pursuit of ISO/IEC certifications for AI safety.
Sources & References
Generated by
Stellitron AI
Data Sources
Exa AI Web Search
ArXiv.org
Crunchbase
References
For inquiries, contact:
contact@stellitron.comThis pitch deck is for illustrative purposes. All financial projections and market data are estimates as of Dec 28, 2025.