1. Short-Circuiting the Pixel Paradigm
In AMI's architecture, 3D Gaussians or Meshes are treated as Auxiliary Decoders. The core policy network does not need to render high-fidelity pixels to know what action to take, significantly reducing latency.
RGB-D + Prompts
Latent State (Z)
Action Trajectory
2. Performance Metrics
By operating in the Latent Space, models achieve higher inference frequencies (Hz). Pixel-space models are limited by rasterization speed, creating a throughput bottleneck.
Action-Centric Latents
Focuses on ΔZ transitions rather than pixel deltas for temporal consistency.
Gaussian Primitives
Used as a structured bias in the encoder to help the model learn geometry faster.
Strategic Partnership Opportunities
Collaborate with AMI Labs to apply latent spatial intelligence breakthroughs to real-world hardware and industrial environments.
Academic Integration
Co-publish on foundational models, Gaussian-as-Prior architectures, and high-speed latent dynamics.
Industrial Automation
Deploy latent-space policies to physical robotic fleets. Ideal for high-speed manipulation and warehouse logistics.
Hardware Co-Design
Optimize sensor placement and compute hardware to better support latent-first inference flows.